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Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
170500096/170498071 [==============================] - 2s 0us/step

train_images:	(50000, 32, 32, 3)
train_labels:	(50000, 1)
test_images:		(10000, 32, 32, 3)
test_labels:		(10000, 1)

array([[[[ 59,  62,  63],
         [ 43,  46,  45],
         [ 50,  48,  43],
         ...,
         [158, 132, 108],
         [152, 125, 102],
         [148, 124, 103]],

        [[ 16,  20,  20],
         [  0,   0,   0],
         [ 18,   8,   0],
         ...,
         [123,  88,  55],
         [119,  83,  50],
         [122,  87,  57]],

        [[ 25,  24,  21],
         [ 16,   7,   0],
         [ 49,  27,   8],
         ...,
         [118,  84,  50],
         [120,  84,  50],
         [109,  73,  42]],

        ...,

        [[208, 170,  96],
         [201, 153,  34],
         [198, 161,  26],
         ...,
         [160, 133,  70],
         [ 56,  31,   7],
         [ 53,  34,  20]],

        [[180, 139,  96],
         [173, 123,  42],
         [186, 144,  30],
         ...,
         [184, 148,  94],
         [ 97,  62,  34],
         [ 83,  53,  34]],

        [[177, 144, 116],
         [168, 129,  94],
         [179, 142,  87],
         ...,
         [216, 184, 140],
         [151, 118,  84],
         [123,  92,  72]]],


       [[[154, 177, 187],
         [126, 137, 136],
         [105, 104,  95],
         ...,
         [ 91,  95,  71],
         [ 87,  90,  71],
         [ 79,  81,  70]],

        [[140, 160, 169],
         [145, 153, 154],
         [125, 125, 118],
         ...,
         [ 96,  99,  78],
         [ 77,  80,  62],
         [ 71,  73,  61]],

        [[140, 155, 164],
         [139, 146, 149],
         [115, 115, 112],
         ...,
         [ 79,  82,  64],
         [ 68,  70,  55],
         [ 67,  69,  55]],

        ...,

        [[175, 167, 166],
         [156, 154, 160],
         [154, 160, 170],
         ...,
         [ 42,  34,  36],
         [ 61,  53,  57],
         [ 93,  83,  91]],

        [[165, 154, 128],
         [156, 152, 130],
         [159, 161, 142],
         ...,
         [103,  93,  96],
         [123, 114, 120],
         [131, 121, 131]],

        [[163, 148, 120],
         [158, 148, 122],
         [163, 156, 133],
         ...,
         [143, 133, 139],
         [143, 134, 142],
         [143, 133, 144]]],


       [[[255, 255, 255],
         [253, 253, 253],
         [253, 253, 253],
         ...,
         [253, 253, 253],
         [253, 253, 253],
         [253, 253, 253]],

        [[255, 255, 255],
         [255, 255, 255],
         [255, 255, 255],
         ...,
         [255, 255, 255],
         [255, 255, 255],
         [255, 255, 255]],

        [[255, 255, 255],
         [254, 254, 254],
         [254, 254, 254],
         ...,
         [254, 254, 254],
         [254, 254, 254],
         [254, 254, 254]],

        ...,

        [[113, 120, 112],
         [111, 118, 111],
         [105, 112, 106],
         ...,
         [ 72,  81,  80],
         [ 72,  80,  79],
         [ 72,  80,  79]],

        [[111, 118, 110],
         [104, 111, 104],
         [ 99, 106,  98],
         ...,
         [ 68,  75,  73],
         [ 70,  76,  75],
         [ 78,  84,  82]],

        [[106, 113, 105],
         [ 99, 106,  98],
         [ 95, 102,  94],
         ...,
         [ 78,  85,  83],
         [ 79,  85,  83],
         [ 80,  86,  84]]],


       ...,


       [[[ 35, 178, 235],
         [ 40, 176, 239],
         [ 42, 176, 241],
         ...,
         [ 99, 177, 219],
         [ 79, 147, 197],
         [ 89, 148, 189]],

        [[ 57, 182, 234],
         [ 44, 184, 250],
         [ 50, 183, 240],
         ...,
         [156, 182, 200],
         [141, 177, 206],
         [116, 149, 175]],

        [[ 98, 197, 237],
         [ 64, 189, 252],
         [ 69, 192, 245],
         ...,
         [188, 195, 206],
         [119, 135, 147],
         [ 61,  79,  90]],

        ...,

        [[ 73,  79,  77],
         [ 53,  63,  68],
         [ 54,  68,  80],
         ...,
         [ 17,  40,  64],
         [ 21,  36,  51],
         [ 33,  48,  49]],

        [[ 61,  68,  75],
         [ 55,  70,  86],
         [ 57,  79, 103],
         ...,
         [ 24,  48,  72],
         [ 17,  35,  53],
         [  7,  23,  32]],

        [[ 44,  56,  73],
         [ 46,  66,  88],
         [ 49,  77, 105],
         ...,
         [ 27,  52,  77],
         [ 21,  43,  66],
         [ 12,  31,  50]]],


       [[[189, 211, 240],
         [186, 208, 236],
         [185, 207, 235],
         ...,
         [175, 195, 224],
         [172, 194, 222],
         [169, 194, 220]],

        [[194, 210, 239],
         [191, 207, 236],
         [190, 206, 235],
         ...,
         [173, 192, 220],
         [171, 191, 218],
         [167, 190, 216]],

        [[208, 219, 244],
         [205, 216, 240],
         [204, 215, 239],
         ...,
         [175, 191, 217],
         [172, 190, 216],
         [169, 191, 215]],

        ...,

        [[207, 199, 181],
         [203, 195, 175],
         [203, 196, 173],
         ...,
         [135, 132, 127],
         [162, 158, 150],
         [168, 163, 151]],

        [[198, 190, 170],
         [189, 181, 159],
         [180, 172, 147],
         ...,
         [178, 171, 160],
         [175, 169, 156],
         [175, 169, 154]],

        [[198, 189, 173],
         [189, 181, 162],
         [178, 170, 149],
         ...,
         [195, 184, 169],
         [196, 189, 171],
         [195, 190, 171]]],


       [[[229, 229, 239],
         [236, 237, 247],
         [234, 236, 247],
         ...,
         [217, 219, 233],
         [221, 223, 234],
         [222, 223, 233]],

        [[222, 221, 229],
         [239, 239, 249],
         [233, 234, 246],
         ...,
         [223, 223, 236],
         [227, 228, 238],
         [210, 211, 220]],

        [[213, 206, 211],
         [234, 232, 239],
         [231, 233, 244],
         ...,
         [220, 220, 232],
         [220, 219, 232],
         [202, 203, 215]],

        ...,

        [[150, 143, 135],
         [140, 135, 127],
         [132, 127, 120],
         ...,
         [224, 222, 218],
         [230, 228, 225],
         [241, 241, 238]],

        [[137, 132, 126],
         [130, 127, 120],
         [125, 121, 115],
         ...,
         [181, 180, 178],
         [202, 201, 198],
         [212, 211, 207]],

        [[122, 119, 114],
         [118, 116, 110],
         [120, 116, 111],
         ...,
         [179, 177, 173],
         [164, 164, 162],
         [163, 163, 161]]]], dtype=uint8)

First ten labels training dataset:
 [[6]
 [9]
 [9]
 [4]
 [1]
 [1]
 [2]
 [7]
 [8]
 [3]]

This output the numeric label, need to convert to item description








((50000, 32, 32, 3), (10000, 32, 32, 3))


((3000, 32, 32, 3), (3000, 1))

((47000, 32, 32, 3), (47000, 1))


Dense Model with Regularization (2 layers)

Code Text






Epoch 1/200
92/92 [==============================] - 2s 7ms/step - loss: 2.8106 - accuracy: 0.2552 - val_loss: 2.3320 - val_accuracy: 0.3117
Epoch 2/200
92/92 [==============================] - 0s 5ms/step - loss: 2.2272 - accuracy: 0.3516 - val_loss: 2.1146 - val_accuracy: 0.3650
Epoch 3/200
92/92 [==============================] - 0s 5ms/step - loss: 2.0765 - accuracy: 0.3777 - val_loss: 1.9831 - val_accuracy: 0.3970
Epoch 4/200
92/92 [==============================] - 0s 5ms/step - loss: 1.9668 - accuracy: 0.3944 - val_loss: 1.8991 - val_accuracy: 0.4060
Epoch 5/200
92/92 [==============================] - 0s 4ms/step - loss: 1.8834 - accuracy: 0.4053 - val_loss: 1.8323 - val_accuracy: 0.4150
Epoch 6/200
92/92 [==============================] - 0s 5ms/step - loss: 1.8249 - accuracy: 0.4161 - val_loss: 1.8191 - val_accuracy: 0.3987
Epoch 7/200
92/92 [==============================] - 0s 5ms/step - loss: 1.7706 - accuracy: 0.4282 - val_loss: 1.7468 - val_accuracy: 0.4300
Epoch 8/200
92/92 [==============================] - 0s 5ms/step - loss: 1.7415 - accuracy: 0.4303 - val_loss: 1.7446 - val_accuracy: 0.4243
Epoch 9/200
92/92 [==============================] - 0s 4ms/step - loss: 1.7072 - accuracy: 0.4389 - val_loss: 1.6909 - val_accuracy: 0.4417
Epoch 10/200
92/92 [==============================] - 0s 5ms/step - loss: 1.6696 - accuracy: 0.4492 - val_loss: 1.6824 - val_accuracy: 0.4357
Epoch 11/200
92/92 [==============================] - 0s 4ms/step - loss: 1.6542 - accuracy: 0.4531 - val_loss: 1.6538 - val_accuracy: 0.4540
Epoch 12/200
92/92 [==============================] - 0s 4ms/step - loss: 1.6498 - accuracy: 0.4480 - val_loss: 1.6476 - val_accuracy: 0.4430
Epoch 13/200
92/92 [==============================] - 0s 5ms/step - loss: 1.6307 - accuracy: 0.4546 - val_loss: 1.6700 - val_accuracy: 0.4317
Epoch 14/200
92/92 [==============================] - 0s 5ms/step - loss: 1.6091 - accuracy: 0.4634 - val_loss: 1.6021 - val_accuracy: 0.4673
Epoch 15/200
92/92 [==============================] - 0s 4ms/step - loss: 1.6002 - accuracy: 0.4667 - val_loss: 1.6153 - val_accuracy: 0.4557
Epoch 16/200
92/92 [==============================] - 0s 5ms/step - loss: 1.5905 - accuracy: 0.4677 - val_loss: 1.6462 - val_accuracy: 0.4430
Epoch 17/200
92/92 [==============================] - 0s 4ms/step - loss: 1.5865 - accuracy: 0.4692 - val_loss: 1.6209 - val_accuracy: 0.4580

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
flatten (Flatten)            (None, 3072)              0         
_________________________________________________________________
dense (Dense)                (None, 384)               1180032   
_________________________________________________________________
dense_1 (Dense)              (None, 10)                3850      
=================================================================
Total params: 1,183,882
Trainable params: 1,183,882
Non-trainable params: 0
_________________________________________________________________

313/313 [==============================] - 1s 2ms/step - loss: 1.6309 - accuracy: 0.4515
test set accuracy:  45.14999985694885

shape of preds:  (10000, 10)

dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])





<tf.Tensor: shape=(10, 10), dtype=int32, numpy=
array([[479,  35,  52,  19,  31,   7,  39,  68, 132, 138],
       [ 38, 506,   9,  18,  13,  18,  27,  53,  36, 282],
       [ 78,  30, 216,  83, 151,  46, 216, 116,  19,  45],
       [ 38,  23,  57, 258,  46, 139, 215,  85,  24, 115],
       [ 55,  13,  74,  38, 336,  33, 253, 143,  17,  38],
       [ 27,  18,  52, 159,  73, 286, 176, 128,  26,  55],
       [  6,  17,  25,  49,  85,  25, 690,  46,  11,  46],
       [ 29,  27,  34,  44,  58,  44,  64, 586,   9, 105],
       [105,  68,  14,  20,  19,  23,  23,  29, 481, 218],
       [ 35, 122,   8,  25,   7,  15,  37,  54,  20, 677]], dtype=int32)>






3

['flatten', 'dense', 'dense_1']



Dense layer without Regularization (1 Layer)

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Epoch 1/200
92/92 [==============================] - 1s 6ms/step - loss: 2.2266 - accuracy: 0.2568 - val_loss: 1.8879 - val_accuracy: 0.3313
Epoch 2/200
92/92 [==============================] - 0s 4ms/step - loss: 1.8584 - accuracy: 0.3500 - val_loss: 1.7947 - val_accuracy: 0.3713
Epoch 3/200
92/92 [==============================] - 0s 4ms/step - loss: 1.7846 - accuracy: 0.3779 - val_loss: 1.7213 - val_accuracy: 0.3963
Epoch 4/200
92/92 [==============================] - 0s 4ms/step - loss: 1.7307 - accuracy: 0.3992 - val_loss: 1.6976 - val_accuracy: 0.4170
Epoch 5/200
92/92 [==============================] - 0s 4ms/step - loss: 1.6958 - accuracy: 0.4093 - val_loss: 1.6905 - val_accuracy: 0.3913
Epoch 6/200
92/92 [==============================] - 0s 4ms/step - loss: 1.6602 - accuracy: 0.4199 - val_loss: 1.6162 - val_accuracy: 0.4373
Epoch 7/200
92/92 [==============================] - 0s 5ms/step - loss: 1.6351 - accuracy: 0.4254 - val_loss: 1.6302 - val_accuracy: 0.4157
Epoch 8/200
92/92 [==============================] - 0s 4ms/step - loss: 1.6026 - accuracy: 0.4398 - val_loss: 1.5811 - val_accuracy: 0.4513
Epoch 9/200
92/92 [==============================] - 0s 4ms/step - loss: 1.5791 - accuracy: 0.4500 - val_loss: 1.5780 - val_accuracy: 0.4387
Epoch 10/200
92/92 [==============================] - 0s 4ms/step - loss: 1.5577 - accuracy: 0.4566 - val_loss: 1.6056 - val_accuracy: 0.4363
Epoch 11/200
92/92 [==============================] - 0s 4ms/step - loss: 1.5434 - accuracy: 0.4586 - val_loss: 1.5560 - val_accuracy: 0.4540
Epoch 12/200
92/92 [==============================] - 0s 4ms/step - loss: 1.5231 - accuracy: 0.4663 - val_loss: 1.5263 - val_accuracy: 0.4647
Epoch 13/200
92/92 [==============================] - 0s 4ms/step - loss: 1.5087 - accuracy: 0.4730 - val_loss: 1.5418 - val_accuracy: 0.4543
Epoch 14/200
92/92 [==============================] - 0s 4ms/step - loss: 1.4913 - accuracy: 0.4812 - val_loss: 1.5406 - val_accuracy: 0.4660
Epoch 15/200
92/92 [==============================] - 0s 4ms/step - loss: 1.4863 - accuracy: 0.4792 - val_loss: 1.5060 - val_accuracy: 0.4690
Epoch 16/200
92/92 [==============================] - 0s 4ms/step - loss: 1.4726 - accuracy: 0.4835 - val_loss: 1.5005 - val_accuracy: 0.4740
Epoch 17/200
92/92 [==============================] - 0s 4ms/step - loss: 1.4531 - accuracy: 0.4921 - val_loss: 1.5131 - val_accuracy: 0.4743
Epoch 18/200
92/92 [==============================] - 0s 4ms/step - loss: 1.4454 - accuracy: 0.4943 - val_loss: 1.4860 - val_accuracy: 0.4793
Epoch 19/200
92/92 [==============================] - 0s 4ms/step - loss: 1.4286 - accuracy: 0.4994 - val_loss: 1.5115 - val_accuracy: 0.4717
Epoch 20/200
92/92 [==============================] - 0s 4ms/step - loss: 1.4149 - accuracy: 0.5053 - val_loss: 1.4775 - val_accuracy: 0.4780
Epoch 21/200
92/92 [==============================] - 0s 4ms/step - loss: 1.4155 - accuracy: 0.5020 - val_loss: 1.4615 - val_accuracy: 0.4873
Epoch 22/200
92/92 [==============================] - 0s 5ms/step - loss: 1.4037 - accuracy: 0.5064 - val_loss: 1.4438 - val_accuracy: 0.4997
Epoch 23/200
92/92 [==============================] - 0s 4ms/step - loss: 1.3939 - accuracy: 0.5106 - val_loss: 1.4597 - val_accuracy: 0.4883
Epoch 24/200
92/92 [==============================] - 0s 4ms/step - loss: 1.3909 - accuracy: 0.5119 - val_loss: 1.4546 - val_accuracy: 0.4923
Epoch 25/200
92/92 [==============================] - 0s 4ms/step - loss: 1.3772 - accuracy: 0.5159 - val_loss: 1.4418 - val_accuracy: 0.5047
Epoch 26/200
92/92 [==============================] - 0s 4ms/step - loss: 1.3647 - accuracy: 0.5241 - val_loss: 1.4262 - val_accuracy: 0.5117
Epoch 27/200
92/92 [==============================] - 0s 4ms/step - loss: 1.3573 - accuracy: 0.5222 - val_loss: 1.4196 - val_accuracy: 0.5087
Epoch 28/200
92/92 [==============================] - 0s 4ms/step - loss: 1.3403 - accuracy: 0.5314 - val_loss: 1.4221 - val_accuracy: 0.5110
Epoch 29/200
92/92 [==============================] - 0s 4ms/step - loss: 1.3516 - accuracy: 0.5256 - val_loss: 1.4211 - val_accuracy: 0.5057

Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
flatten_1 (Flatten)          (None, 3072)              0         
_________________________________________________________________
dense_2 (Dense)              (None, 384)               1180032   
_________________________________________________________________
dense_3 (Dense)              (None, 10)                3850      
=================================================================
Total params: 1,183,882
Trainable params: 1,183,882
Non-trainable params: 0
_________________________________________________________________
Code Text

313/313 [==============================] - 1s 2ms/step - loss: 1.4496 - accuracy: 0.4892
test set accuracy:  48.91999959945679
Code Text

shape of preds:  (10000, 10)

dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])





<tf.Tensor: shape=(10, 10), dtype=int32, numpy=
array([[440,  31, 106,  34,  53,  20,  19,  82, 156,  59],
       [ 24, 566,  16,  25,  20,  12,  17,  44,  77, 199],
       [ 52,  24, 366,  82, 146,  75,  95, 121,  19,  20],
       [ 18,  15, 101, 344,  72, 177,  99,  95,  18,  61],
       [ 33,  10, 155,  59, 427,  38,  98, 138,  22,  20],
       [  8,  11,  97, 217,  90, 330,  69, 122,  27,  29],
       [  4,  13,  90,  90, 146,  43, 520,  51,  18,  25],
       [ 17,  12,  51,  57,  79,  60,  14, 651,  13,  46],
       [ 62,  65,  20,  28,  38,  21,   6,  42, 656,  62],
       [ 25, 149,  15,  42,  14,  27,  14,  63,  59, 592]], dtype=int32)>






Dense layer without regularization (2 layers)

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Epoch 1/200
92/92 [==============================] - 1s 6ms/step - loss: 2.0320 - accuracy: 0.2773 - val_loss: 1.8437 - val_accuracy: 0.3337
Epoch 2/200
92/92 [==============================] - 0s 5ms/step - loss: 1.8163 - accuracy: 0.3616 - val_loss: 1.7548 - val_accuracy: 0.3783
Epoch 3/200
92/92 [==============================] - 0s 4ms/step - loss: 1.7165 - accuracy: 0.3983 - val_loss: 1.6428 - val_accuracy: 0.4160
Epoch 4/200
92/92 [==============================] - 0s 4ms/step - loss: 1.6398 - accuracy: 0.4223 - val_loss: 1.6231 - val_accuracy: 0.4253
Epoch 5/200
92/92 [==============================] - 0s 5ms/step - loss: 1.5989 - accuracy: 0.4379 - val_loss: 1.5628 - val_accuracy: 0.4440
Epoch 6/200
92/92 [==============================] - 0s 4ms/step - loss: 1.5569 - accuracy: 0.4509 - val_loss: 1.5561 - val_accuracy: 0.4440
Epoch 7/200
92/92 [==============================] - 0s 5ms/step - loss: 1.5200 - accuracy: 0.4636 - val_loss: 1.5326 - val_accuracy: 0.4590
Epoch 8/200
92/92 [==============================] - 0s 5ms/step - loss: 1.4935 - accuracy: 0.4721 - val_loss: 1.4927 - val_accuracy: 0.4737
Epoch 9/200
92/92 [==============================] - 0s 5ms/step - loss: 1.4582 - accuracy: 0.4829 - val_loss: 1.4689 - val_accuracy: 0.4743
Epoch 10/200
92/92 [==============================] - 0s 5ms/step - loss: 1.4416 - accuracy: 0.4918 - val_loss: 1.4801 - val_accuracy: 0.4840
Epoch 11/200
92/92 [==============================] - 0s 5ms/step - loss: 1.4138 - accuracy: 0.5012 - val_loss: 1.4305 - val_accuracy: 0.4907
Epoch 12/200
92/92 [==============================] - 0s 5ms/step - loss: 1.3992 - accuracy: 0.5050 - val_loss: 1.4198 - val_accuracy: 0.4903
Epoch 13/200
92/92 [==============================] - 0s 5ms/step - loss: 1.3763 - accuracy: 0.5130 - val_loss: 1.4187 - val_accuracy: 0.4980
Epoch 14/200
92/92 [==============================] - 0s 5ms/step - loss: 1.3529 - accuracy: 0.5230 - val_loss: 1.3895 - val_accuracy: 0.5100
Epoch 15/200
92/92 [==============================] - 0s 4ms/step - loss: 1.3449 - accuracy: 0.5254 - val_loss: 1.3958 - val_accuracy: 0.5110
Epoch 16/200
92/92 [==============================] - 0s 4ms/step - loss: 1.3185 - accuracy: 0.5322 - val_loss: 1.3802 - val_accuracy: 0.5177
Epoch 17/200
92/92 [==============================] - 0s 4ms/step - loss: 1.3089 - accuracy: 0.5379 - val_loss: 1.3791 - val_accuracy: 0.5143
Epoch 18/200
92/92 [==============================] - 0s 5ms/step - loss: 1.2883 - accuracy: 0.5460 - val_loss: 1.3839 - val_accuracy: 0.5150
Epoch 19/200
92/92 [==============================] - 0s 5ms/step - loss: 1.2673 - accuracy: 0.5557 - val_loss: 1.3769 - val_accuracy: 0.5167

Model: "sequential_2"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
flatten_2 (Flatten)          (None, 3072)              0         
_________________________________________________________________
dense_4 (Dense)              (None, 384)               1180032   
_________________________________________________________________
dense_5 (Dense)              (None, 192)               73920     
_________________________________________________________________
dense_6 (Dense)              (None, 10)                1930      
=================================================================
Total params: 1,255,882
Trainable params: 1,255,882
Non-trainable params: 0
_________________________________________________________________

313/313 [==============================] - 1s 2ms/step - loss: 1.3997 - accuracy: 0.5015
test set accuracy:  50.15000104904175

shape of preds:  (10000, 10)

dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])





<tf.Tensor: shape=(10, 10), dtype=int32, numpy=
array([[731,  63,  25,  11,  25,  13,  24,  18,  53,  37],
       [ 70, 695,  19,  13,   6,  14,  16,  22,  31, 114],
       [153,  29, 344,  84, 142,  59, 102,  64,  12,  11],
       [ 75,  36,  84, 292,  59, 195, 136,  55,  26,  42],
       [108,  21, 128,  52, 431,  42, 108,  85,  12,  13],
       [ 63,  25,  83, 179,  69, 390,  75,  75,  22,  19],
       [ 30,  17,  59,  69, 130,  48, 593,  27,  15,  12],
       [ 92,  26,  48,  48,  88,  63,  20, 556,  11,  48],
       [266, 112,   8,  19,  17,  19,   6,  18, 485,  50],
       [ 77, 263,   7,  32,  13,  20,  23,  40,  27, 498]], dtype=int32)>






Dense layer without regularization (3 layers)

Code Text






Epoch 1/200
92/92 [==============================] - 1s 6ms/step - loss: 1.9800 - accuracy: 0.2914 - val_loss: 1.7839 - val_accuracy: 0.3553
Epoch 2/200
92/92 [==============================] - 0s 5ms/step - loss: 1.7702 - accuracy: 0.3701 - val_loss: 1.7052 - val_accuracy: 0.3923
Epoch 3/200
92/92 [==============================] - 0s 5ms/step - loss: 1.6758 - accuracy: 0.4021 - val_loss: 1.6422 - val_accuracy: 0.4177
Epoch 4/200
92/92 [==============================] - 0s 5ms/step - loss: 1.6115 - accuracy: 0.4282 - val_loss: 1.5511 - val_accuracy: 0.4590
Epoch 5/200
92/92 [==============================] - 0s 5ms/step - loss: 1.5594 - accuracy: 0.4465 - val_loss: 1.5503 - val_accuracy: 0.4553
Epoch 6/200
92/92 [==============================] - 0s 5ms/step - loss: 1.5268 - accuracy: 0.4560 - val_loss: 1.5233 - val_accuracy: 0.4527
Epoch 7/200
92/92 [==============================] - 0s 5ms/step - loss: 1.4939 - accuracy: 0.4693 - val_loss: 1.4990 - val_accuracy: 0.4643
Epoch 8/200
92/92 [==============================] - 0s 5ms/step - loss: 1.4687 - accuracy: 0.4795 - val_loss: 1.4700 - val_accuracy: 0.4790
Epoch 9/200
92/92 [==============================] - 0s 5ms/step - loss: 1.4341 - accuracy: 0.4919 - val_loss: 1.4477 - val_accuracy: 0.4773
Epoch 10/200
92/92 [==============================] - 0s 5ms/step - loss: 1.4243 - accuracy: 0.4940 - val_loss: 1.4520 - val_accuracy: 0.4717
Epoch 11/200
92/92 [==============================] - 0s 5ms/step - loss: 1.3948 - accuracy: 0.5058 - val_loss: 1.4182 - val_accuracy: 0.4977
Epoch 12/200
92/92 [==============================] - 0s 5ms/step - loss: 1.3766 - accuracy: 0.5096 - val_loss: 1.4154 - val_accuracy: 0.4930
Epoch 13/200
92/92 [==============================] - 0s 5ms/step - loss: 1.3515 - accuracy: 0.5205 - val_loss: 1.4243 - val_accuracy: 0.4960
Epoch 14/200
92/92 [==============================] - 0s 5ms/step - loss: 1.3429 - accuracy: 0.5240 - val_loss: 1.4110 - val_accuracy: 0.4987
Epoch 15/200
92/92 [==============================] - 0s 5ms/step - loss: 1.3307 - accuracy: 0.5278 - val_loss: 1.3873 - val_accuracy: 0.5083
Epoch 16/200
92/92 [==============================] - 0s 5ms/step - loss: 1.3003 - accuracy: 0.5393 - val_loss: 1.3874 - val_accuracy: 0.5077
Epoch 17/200
92/92 [==============================] - 0s 5ms/step - loss: 1.2807 - accuracy: 0.5454 - val_loss: 1.4546 - val_accuracy: 0.4857
Epoch 18/200
92/92 [==============================] - 0s 5ms/step - loss: 1.2717 - accuracy: 0.5493 - val_loss: 1.4245 - val_accuracy: 0.4997

Model: "sequential_3"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
flatten_3 (Flatten)          (None, 3072)              0         
_________________________________________________________________
dense_7 (Dense)              (None, 384)               1180032   
_________________________________________________________________
dense_8 (Dense)              (None, 192)               73920     
_________________________________________________________________
dense_9 (Dense)              (None, 96)                18528     
_________________________________________________________________
dense_10 (Dense)             (None, 10)                970       
=================================================================
Total params: 1,273,450
Trainable params: 1,273,450
Non-trainable params: 0
_________________________________________________________________

313/313 [==============================] - 1s 2ms/step - loss: 1.4402 - accuracy: 0.4893
test set accuracy:  48.9300012588501

shape of preds:  (10000, 10)

dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])





<tf.Tensor: shape=(10, 10), dtype=int32, numpy=
array([[619,  24,  72,  23,  11,  76,  10,  58,  53,  54],
       [ 48, 555,  29,  29,   5,  39,  12,  43,  33, 207],
       [ 73,  15, 437,  75,  51, 170,  66,  90,   8,  15],
       [ 21,   7, 104, 302,  28, 365,  62,  72,  11,  28],
       [ 54,  10, 243,  66, 277, 123,  85, 122,   7,  13],
       [ 22,   3,  91, 144,  25, 572,  40,  80,  11,  12],
       [  8,   8, 146, 100,  58, 153, 475,  34,   4,  14],
       [ 32,   9,  54,  58,  46, 131,  16, 619,   3,  32],
       [222,  53,  22,  29,  10,  79,   5,  28, 446, 106],
       [ 52, 122,  17,  42,   8,  57,  15,  73,  23, 591]], dtype=int32)>





CNN without regularization (2 layers)

Code Text

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Epoch 1/200
92/92 [==============================] - 18s 37ms/step - loss: 1.7844 - accuracy: 0.3575 - val_loss: 1.3712 - val_accuracy: 0.5273
Epoch 2/200
92/92 [==============================] - 3s 29ms/step - loss: 1.3449 - accuracy: 0.5219 - val_loss: 1.1917 - val_accuracy: 0.5877
Epoch 3/200
92/92 [==============================] - 3s 29ms/step - loss: 1.1762 - accuracy: 0.5882 - val_loss: 1.0742 - val_accuracy: 0.6347
Epoch 4/200
92/92 [==============================] - 3s 29ms/step - loss: 1.0663 - accuracy: 0.6269 - val_loss: 0.9765 - val_accuracy: 0.6733
Epoch 5/200
92/92 [==============================] - 3s 29ms/step - loss: 0.9972 - accuracy: 0.6525 - val_loss: 0.9596 - val_accuracy: 0.6637
Epoch 6/200
92/92 [==============================] - 3s 30ms/step - loss: 0.9350 - accuracy: 0.6755 - val_loss: 0.8773 - val_accuracy: 0.7033
Epoch 7/200
92/92 [==============================] - 3s 29ms/step - loss: 0.8780 - accuracy: 0.6920 - val_loss: 0.8470 - val_accuracy: 0.7160
Epoch 8/200
92/92 [==============================] - 3s 29ms/step - loss: 0.8235 - accuracy: 0.7155 - val_loss: 0.7987 - val_accuracy: 0.7307
Epoch 9/200
92/92 [==============================] - 3s 29ms/step - loss: 0.7815 - accuracy: 0.7279 - val_loss: 0.7893 - val_accuracy: 0.7323
Epoch 10/200
92/92 [==============================] - 3s 29ms/step - loss: 0.7501 - accuracy: 0.7396 - val_loss: 0.7685 - val_accuracy: 0.7357
Epoch 11/200
92/92 [==============================] - 3s 29ms/step - loss: 0.7067 - accuracy: 0.7534 - val_loss: 0.7794 - val_accuracy: 0.7360
Epoch 12/200
92/92 [==============================] - 3s 29ms/step - loss: 0.6716 - accuracy: 0.7659 - val_loss: 0.7211 - val_accuracy: 0.7543
Epoch 13/200
92/92 [==============================] - 3s 30ms/step - loss: 0.6369 - accuracy: 0.7782 - val_loss: 0.7137 - val_accuracy: 0.7547
Epoch 14/200
92/92 [==============================] - 3s 29ms/step - loss: 0.5966 - accuracy: 0.7921 - val_loss: 0.7208 - val_accuracy: 0.7603
Epoch 15/200
92/92 [==============================] - 3s 29ms/step - loss: 0.5599 - accuracy: 0.8066 - val_loss: 0.6875 - val_accuracy: 0.7640
Epoch 16/200
92/92 [==============================] - 3s 29ms/step - loss: 0.5255 - accuracy: 0.8169 - val_loss: 0.7267 - val_accuracy: 0.7573
Epoch 17/200
92/92 [==============================] - 3s 30ms/step - loss: 0.4965 - accuracy: 0.8280 - val_loss: 0.6871 - val_accuracy: 0.7680
Epoch 18/200
92/92 [==============================] - 3s 30ms/step - loss: 0.4576 - accuracy: 0.8432 - val_loss: 0.6955 - val_accuracy: 0.7697
Epoch 19/200
92/92 [==============================] - 3s 29ms/step - loss: 0.4275 - accuracy: 0.8505 - val_loss: 0.6957 - val_accuracy: 0.7577
Epoch 20/200
92/92 [==============================] - 3s 30ms/step - loss: 0.3950 - accuracy: 0.8617 - val_loss: 0.7068 - val_accuracy: 0.7677
Epoch 21/200
92/92 [==============================] - 3s 30ms/step - loss: 0.3676 - accuracy: 0.8722 - val_loss: 0.7026 - val_accuracy: 0.7673

Model: "sequential_4"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, 30, 30, 128)       3584      
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 15, 15, 128)       0         
_________________________________________________________________
dropout (Dropout)            (None, 15, 15, 128)       0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 13, 13, 256)       295168    
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 6, 6, 256)         0         
_________________________________________________________________
dropout_1 (Dropout)          (None, 6, 6, 256)         0         
_________________________________________________________________
flatten_4 (Flatten)          (None, 9216)              0         
_________________________________________________________________
dense_11 (Dense)             (None, 384)               3539328   
_________________________________________________________________
dense_12 (Dense)             (None, 10)                3850      
=================================================================
Total params: 3,841,930
Trainable params: 3,841,930
Non-trainable params: 0
_________________________________________________________________

313/313 [==============================] - 1s 3ms/step - loss: 0.7501 - accuracy: 0.7515
test set accuracy:  75.15000104904175
Code Text

shape of preds:  (10000, 10)

dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])





<tf.Tensor: shape=(10, 10), dtype=int32, numpy=
array([[836,  17,  32,   8,  19,   7,   9,  10,  32,  30],
       [ 19, 833,   7,   3,   3,   5,  15,   2,  18,  95],
       [ 69,   2, 620,  44,  76,  55,  76,  35,  10,  13],
       [ 29,   4,  67, 469,  69, 201,  93,  42,  10,  16],
       [ 12,   2,  52,  36, 732,  35,  56,  63,   7,   5],
       [ 16,   0,  36, 134,  45, 670,  35,  50,   3,  11],
       [  4,   4,  32,  28,  28,  17, 876,   4,   4,   3],
       [ 18,   3,  20,  22,  46,  52,   9, 804,   2,  24],
       [ 63,  30,  18,   8,   5,   9,  10,   4, 821,  32],
       [ 31,  47,   7,   6,   5,   7,   9,  14,  20, 854]], dtype=int32)>






8

['conv2d',
 'max_pooling2d',
 'dropout',
 'conv2d_1',
 'max_pooling2d_1',
 'dropout_1',
 'flatten_4',
 'dense_11',
 'dense_12']









CNN without regularization (3 layers)

Code Text

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Epoch 1/200
92/92 [==============================] - 4s 37ms/step - loss: 1.9011 - accuracy: 0.2919 - val_loss: 1.5971 - val_accuracy: 0.4157
Epoch 2/200
92/92 [==============================] - 3s 36ms/step - loss: 1.4398 - accuracy: 0.4783 - val_loss: 1.2686 - val_accuracy: 0.5427
Epoch 3/200
92/92 [==============================] - 3s 36ms/step - loss: 1.2529 - accuracy: 0.5549 - val_loss: 1.0766 - val_accuracy: 0.6270
Epoch 4/200
92/92 [==============================] - 3s 36ms/step - loss: 1.1115 - accuracy: 0.6080 - val_loss: 0.9797 - val_accuracy: 0.6617
Epoch 5/200
92/92 [==============================] - 3s 36ms/step - loss: 1.0099 - accuracy: 0.6475 - val_loss: 0.8825 - val_accuracy: 0.6910
Epoch 6/200
92/92 [==============================] - 3s 36ms/step - loss: 0.9372 - accuracy: 0.6707 - val_loss: 0.8058 - val_accuracy: 0.7217
Epoch 7/200
92/92 [==============================] - 3s 36ms/step - loss: 0.8683 - accuracy: 0.6977 - val_loss: 0.7991 - val_accuracy: 0.7230
Epoch 8/200
92/92 [==============================] - 3s 36ms/step - loss: 0.8129 - accuracy: 0.7136 - val_loss: 0.7217 - val_accuracy: 0.7477
Epoch 9/200
92/92 [==============================] - 3s 36ms/step - loss: 0.7694 - accuracy: 0.7311 - val_loss: 0.7155 - val_accuracy: 0.7610
Epoch 10/200
92/92 [==============================] - 3s 35ms/step - loss: 0.7298 - accuracy: 0.7441 - val_loss: 0.6809 - val_accuracy: 0.7703
Epoch 11/200
92/92 [==============================] - 3s 36ms/step - loss: 0.6851 - accuracy: 0.7593 - val_loss: 0.6510 - val_accuracy: 0.7787
Epoch 12/200
92/92 [==============================] - 3s 35ms/step - loss: 0.6507 - accuracy: 0.7719 - val_loss: 0.6268 - val_accuracy: 0.7847
Epoch 13/200
92/92 [==============================] - 3s 36ms/step - loss: 0.6160 - accuracy: 0.7848 - val_loss: 0.6192 - val_accuracy: 0.7897
Epoch 14/200
92/92 [==============================] - 3s 36ms/step - loss: 0.5967 - accuracy: 0.7914 - val_loss: 0.6098 - val_accuracy: 0.7880
Epoch 15/200
92/92 [==============================] - 3s 36ms/step - loss: 0.5640 - accuracy: 0.8024 - val_loss: 0.6300 - val_accuracy: 0.7803
Epoch 16/200
92/92 [==============================] - 3s 35ms/step - loss: 0.5421 - accuracy: 0.8104 - val_loss: 0.6057 - val_accuracy: 0.8017
Epoch 17/200
92/92 [==============================] - 3s 35ms/step - loss: 0.5196 - accuracy: 0.8169 - val_loss: 0.5776 - val_accuracy: 0.8003
Epoch 18/200
92/92 [==============================] - 3s 35ms/step - loss: 0.4957 - accuracy: 0.8269 - val_loss: 0.5683 - val_accuracy: 0.8070
Epoch 19/200
92/92 [==============================] - 3s 36ms/step - loss: 0.4729 - accuracy: 0.8336 - val_loss: 0.5641 - val_accuracy: 0.8070
Epoch 20/200
92/92 [==============================] - 3s 36ms/step - loss: 0.4509 - accuracy: 0.8401 - val_loss: 0.5778 - val_accuracy: 0.8010
Epoch 21/200
92/92 [==============================] - 3s 36ms/step - loss: 0.4255 - accuracy: 0.8504 - val_loss: 0.5631 - val_accuracy: 0.8027
Code Text

Model: "sequential_19"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_40 (Conv2D)           (None, 30, 30, 128)       3584      
_________________________________________________________________
max_pooling2d_34 (MaxPooling (None, 15, 15, 128)       0         
_________________________________________________________________
dropout_36 (Dropout)         (None, 15, 15, 128)       0         
_________________________________________________________________
conv2d_41 (Conv2D)           (None, 13, 13, 256)       295168    
_________________________________________________________________
max_pooling2d_35 (MaxPooling (None, 6, 6, 256)         0         
_________________________________________________________________
dropout_37 (Dropout)         (None, 6, 6, 256)         0         
_________________________________________________________________
conv2d_42 (Conv2D)           (None, 4, 4, 512)         1180160   
_________________________________________________________________
max_pooling2d_36 (MaxPooling (None, 2, 2, 512)         0         
_________________________________________________________________
dropout_38 (Dropout)         (None, 2, 2, 512)         0         
_________________________________________________________________
flatten_19 (Flatten)         (None, 2048)              0         
_________________________________________________________________
dense_46 (Dense)             (None, 384)               786816    
_________________________________________________________________
dense_47 (Dense)             (None, 10)                3850      
=================================================================
Total params: 2,269,578
Trainable params: 2,269,578
Non-trainable params: 0
_________________________________________________________________

313/313 [==============================] - 1s 3ms/step - loss: 0.6289 - accuracy: 0.7872
test set accuracy:  78.71999740600586
Code Text

WARNING:tensorflow:5 out of the last 1256 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fd9b262d3b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
shape of preds:  (10000, 10)
Code Text

dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])
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<tf.Tensor: shape=(10, 10), dtype=int32, numpy=
array([[775,  18,  41,   8,  30,   4,   5,   9,  85,  25],
       [ 13, 881,   5,   1,   1,   4,   6,   2,  28,  59],
       [ 38,   5, 715,  25,  59,  53,  51,  27,  19,   8],
       [ 11,   5,  80, 531,  74, 157,  62,  36,  18,  26],
       [  9,   1,  53,  28, 756,  30,  49,  62,  10,   2],
       [ 10,   3,  38,  98,  43, 713,  20,  55,  14,   6],
       [  4,   6,  33,  28,  23,  15, 872,   6,   8,   5],
       [ 12,   3,  16,  17,  31,  49,   3, 848,   6,  15],
       [ 22,  16,   5,   7,   5,   2,   5,   1, 916,  21],
       [ 14,  55,   7,   7,   4,   2,   5,   7,  34, 865]], dtype=int32)>

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8
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['conv2d_40',
 'max_pooling2d_34',
 'dropout_36',
 'conv2d_41',
 'max_pooling2d_35',
 'dropout_37',
 'conv2d_42',
 'max_pooling2d_36',
 'dropout_38',
 'flatten_19',
 'dense_46',
 'dense_47']
Code Text

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DNN with regularization (2 layers)

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Epoch 1/200
92/92 [==============================] - 1s 7ms/step - loss: 2.6938 - accuracy: 0.2672 - val_loss: 2.3271 - val_accuracy: 0.3400
Epoch 2/200
92/92 [==============================] - 0s 5ms/step - loss: 2.1956 - accuracy: 0.3636 - val_loss: 2.0490 - val_accuracy: 0.3920
Epoch 3/200
92/92 [==============================] - 0s 5ms/step - loss: 2.0086 - accuracy: 0.3976 - val_loss: 1.9298 - val_accuracy: 0.4087
Epoch 4/200
92/92 [==============================] - 0s 5ms/step - loss: 1.9024 - accuracy: 0.4159 - val_loss: 1.8673 - val_accuracy: 0.4087
Epoch 5/200
92/92 [==============================] - 0s 5ms/step - loss: 1.8256 - accuracy: 0.4290 - val_loss: 1.8036 - val_accuracy: 0.4280
Epoch 6/200
92/92 [==============================] - 0s 5ms/step - loss: 1.7644 - accuracy: 0.4421 - val_loss: 1.7574 - val_accuracy: 0.4300
Epoch 7/200
92/92 [==============================] - 0s 5ms/step - loss: 1.7093 - accuracy: 0.4546 - val_loss: 1.6947 - val_accuracy: 0.4480
Epoch 8/200
92/92 [==============================] - 0s 5ms/step - loss: 1.6638 - accuracy: 0.4645 - val_loss: 1.6661 - val_accuracy: 0.4583
Epoch 9/200
92/92 [==============================] - 0s 5ms/step - loss: 1.6421 - accuracy: 0.4707 - val_loss: 1.6521 - val_accuracy: 0.4617
Epoch 10/200
92/92 [==============================] - 0s 5ms/step - loss: 1.6201 - accuracy: 0.4709 - val_loss: 1.6200 - val_accuracy: 0.4733
Epoch 11/200
92/92 [==============================] - 0s 5ms/step - loss: 1.6103 - accuracy: 0.4777 - val_loss: 1.6378 - val_accuracy: 0.4537
Epoch 12/200
92/92 [==============================] - 0s 5ms/step - loss: 1.5883 - accuracy: 0.4806 - val_loss: 1.5932 - val_accuracy: 0.4793
Epoch 13/200
92/92 [==============================] - 0s 5ms/step - loss: 1.5524 - accuracy: 0.4956 - val_loss: 1.5712 - val_accuracy: 0.4773
Epoch 14/200
92/92 [==============================] - 0s 5ms/step - loss: 1.5542 - accuracy: 0.4917 - val_loss: 1.5576 - val_accuracy: 0.4923
Epoch 15/200
92/92 [==============================] - 0s 5ms/step - loss: 1.5349 - accuracy: 0.4988 - val_loss: 1.5700 - val_accuracy: 0.4820
Epoch 16/200
92/92 [==============================] - 0s 5ms/step - loss: 1.5169 - accuracy: 0.5056 - val_loss: 1.5511 - val_accuracy: 0.4923
Epoch 17/200
92/92 [==============================] - 0s 5ms/step - loss: 1.5026 - accuracy: 0.5119 - val_loss: 1.5799 - val_accuracy: 0.4857
Code Text

Model: "sequential_6"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
flatten_6 (Flatten)          (None, 3072)              0         
_________________________________________________________________
dense_15 (Dense)             (None, 384)               1180032   
_________________________________________________________________
dense_16 (Dense)             (None, 192)               73920     
_________________________________________________________________
dense_17 (Dense)             (None, 10)                1930      
=================================================================
Total params: 1,255,882
Trainable params: 1,255,882
Non-trainable params: 0
_________________________________________________________________

313/313 [==============================] - 1s 2ms/step - loss: 1.5986 - accuracy: 0.4762
test set accuracy:  47.620001435279846
Code Text

shape of preds:  (10000, 10)

dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])





<tf.Tensor: shape=(10, 10), dtype=int32, numpy=
array([[707,  57,  24,   7,  32,  11,   8,   8,  85,  61],
       [ 57, 698,  10,   7,  15,   8,   4,  13,  48, 140],
       [182,  44, 273,  46, 229,  68,  48,  49,  30,  31],
       [105,  70,  79, 237, 132, 160,  52,  42,  40,  83],
       [114,  42, 103,  31, 552,  25,  38,  51,  27,  17],
       [ 87,  42,  90, 148, 119, 343,  30,  57,  35,  49],
       [ 39,  49,  78,  71, 251,  72, 361,  22,  19,  38],
       [118,  43,  41,  42, 144,  46,  18, 436,  18,  94],
       [188,  98,   4,   8,  24,   9,   2,   8, 587,  72],
       [ 75, 239,   8,  13,  12,  12,  10,  20,  43, 568]], dtype=int32)>





DNN with regularization (3 layers)

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Epoch 1/200
92/92 [==============================] - 1s 7ms/step - loss: 2.7077 - accuracy: 0.2755 - val_loss: 2.3148 - val_accuracy: 0.3360
Epoch 2/200
92/92 [==============================] - 0s 5ms/step - loss: 2.1770 - accuracy: 0.3594 - val_loss: 2.0361 - val_accuracy: 0.3750
Epoch 3/200
92/92 [==============================] - 0s 5ms/step - loss: 1.9866 - accuracy: 0.3905 - val_loss: 1.8983 - val_accuracy: 0.4060
Epoch 4/200
92/92 [==============================] - 0s 5ms/step - loss: 1.8736 - accuracy: 0.4150 - val_loss: 1.8012 - val_accuracy: 0.4293
Epoch 5/200
92/92 [==============================] - 0s 5ms/step - loss: 1.8088 - accuracy: 0.4289 - val_loss: 1.7892 - val_accuracy: 0.4290
Epoch 6/200
92/92 [==============================] - 0s 5ms/step - loss: 1.7525 - accuracy: 0.4436 - val_loss: 1.7006 - val_accuracy: 0.4513
Epoch 7/200
92/92 [==============================] - 0s 5ms/step - loss: 1.7172 - accuracy: 0.4512 - val_loss: 1.6851 - val_accuracy: 0.4570
Epoch 8/200
92/92 [==============================] - 0s 5ms/step - loss: 1.6739 - accuracy: 0.4630 - val_loss: 1.6636 - val_accuracy: 0.4647
Epoch 9/200
92/92 [==============================] - 0s 5ms/step - loss: 1.6559 - accuracy: 0.4679 - val_loss: 1.6530 - val_accuracy: 0.4583
Epoch 10/200
92/92 [==============================] - 0s 5ms/step - loss: 1.6333 - accuracy: 0.4750 - val_loss: 1.6764 - val_accuracy: 0.4567
Epoch 11/200
92/92 [==============================] - 0s 5ms/step - loss: 1.6118 - accuracy: 0.4817 - val_loss: 1.6240 - val_accuracy: 0.4723
Epoch 12/200
92/92 [==============================] - 0s 5ms/step - loss: 1.5987 - accuracy: 0.4832 - val_loss: 1.6579 - val_accuracy: 0.4617
Epoch 13/200
92/92 [==============================] - 0s 5ms/step - loss: 1.5928 - accuracy: 0.4853 - val_loss: 1.6160 - val_accuracy: 0.4823
Epoch 14/200
92/92 [==============================] - 0s 5ms/step - loss: 1.5717 - accuracy: 0.4956 - val_loss: 1.5734 - val_accuracy: 0.5000
Epoch 15/200
92/92 [==============================] - 0s 5ms/step - loss: 1.5641 - accuracy: 0.4940 - val_loss: 1.5605 - val_accuracy: 0.4910
Epoch 16/200
92/92 [==============================] - 0s 5ms/step - loss: 1.5284 - accuracy: 0.5101 - val_loss: 1.5446 - val_accuracy: 0.5043
Epoch 17/200
92/92 [==============================] - 0s 5ms/step - loss: 1.5356 - accuracy: 0.5043 - val_loss: 1.5549 - val_accuracy: 0.4957
Epoch 18/200
92/92 [==============================] - 0s 5ms/step - loss: 1.5193 - accuracy: 0.5123 - val_loss: 1.5730 - val_accuracy: 0.4850
Epoch 19/200
92/92 [==============================] - 0s 5ms/step - loss: 1.5092 - accuracy: 0.5147 - val_loss: 1.5423 - val_accuracy: 0.5013
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Model: "sequential_7"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
flatten_7 (Flatten)          (None, 3072)              0         
_________________________________________________________________
dense_18 (Dense)             (None, 384)               1180032   
_________________________________________________________________
dense_19 (Dense)             (None, 192)               73920     
_________________________________________________________________
dense_20 (Dense)             (None, 96)                18528     
_________________________________________________________________
dense_21 (Dense)             (None, 10)                970       
=================================================================
Total params: 1,273,450
Trainable params: 1,273,450
Non-trainable params: 0
_________________________________________________________________
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313/313 [==============================] - 1s 2ms/step - loss: 1.5492 - accuracy: 0.5023
test set accuracy:  50.23000240325928
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shape of preds:  (10000, 10)

dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])





<tf.Tensor: shape=(10, 10), dtype=int32, numpy=
array([[529,  30,  88,  38,  21,   6,  24,  32, 186,  46],
       [ 42, 615,  11,  41,  12,   5,  16,  24, 115, 119],
       [ 58,  12, 386, 134, 112,  47, 117,  78,  35,  21],
       [ 25,  23,  93, 422,  42, 101, 134,  57,  45,  58],
       [ 50,  12, 165,  85, 388,  25, 122,  99,  41,  13],
       [ 23,  12,  94, 312,  46, 261,  87,  84,  51,  30],
       [  5,  13,  78, 105, 113,  24, 592,  24,  24,  22],
       [ 38,  15,  54,  96,  75,  38,  30, 571,  33,  50],
       [ 81,  53,  15,  32,  13,   6,  13,  12, 717,  58],
       [ 35, 185,  10,  48,  14,  11,  19,  43,  93, 542]], dtype=int32)>






DNN with alternate regularization (3 layers)

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Epoch 1/200
92/92 [==============================] - 1s 7ms/step - loss: 14.1347 - accuracy: 0.2492 - val_loss: 3.6442 - val_accuracy: 0.2600
Epoch 2/200
92/92 [==============================] - 0s 5ms/step - loss: 2.7269 - accuracy: 0.2624 - val_loss: 2.2832 - val_accuracy: 0.2737
Epoch 3/200
92/92 [==============================] - 0s 5ms/step - loss: 2.2308 - accuracy: 0.2528 - val_loss: 2.1515 - val_accuracy: 0.2850
Epoch 4/200
92/92 [==============================] - 0s 5ms/step - loss: 2.1600 - accuracy: 0.2582 - val_loss: 2.1831 - val_accuracy: 0.2313
Epoch 5/200
92/92 [==============================] - 0s 5ms/step - loss: 2.1374 - accuracy: 0.2523 - val_loss: 2.0936 - val_accuracy: 0.2683
Epoch 6/200
92/92 [==============================] - 0s 5ms/step - loss: 2.1204 - accuracy: 0.2519 - val_loss: 2.0971 - val_accuracy: 0.2703
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Model: "sequential_8"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
flatten_8 (Flatten)          (None, 3072)              0         
_________________________________________________________________
dense_22 (Dense)             (None, 384)               1180032   
_________________________________________________________________
dense_23 (Dense)             (None, 192)               73920     
_________________________________________________________________
dense_24 (Dense)             (None, 96)                18528     
_________________________________________________________________
dense_25 (Dense)             (None, 10)                970       
=================================================================
Total params: 1,273,450
Trainable params: 1,273,450
Non-trainable params: 0
_________________________________________________________________

313/313 [==============================] - 1s 2ms/step - loss: 2.1102 - accuracy: 0.2612
test set accuracy:  26.120001077651978
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shape of preds:  (10000, 10)

dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])





<tf.Tensor: shape=(10, 10), dtype=int32, numpy=
array([[293,  80,   0,   6,  33, 149,  54,  49, 273,  63],
       [ 32, 305,   1,  10,  33,  49, 194,  43,  79, 254],
       [ 95,  58,   1,   3, 226, 161, 341,  59,  45,  11],
       [ 37,  94,   0,   8, 223, 190, 344,  76,  15,  13],
       [ 30,  53,   0,   3, 302, 115, 414,  52,  25,   6],
       [ 49,  47,   0,   6, 257, 263, 291,  64,  18,   5],
       [  5,  68,   0,   4, 161,  64, 637,  55,   2,   4],
       [ 46, 117,   1,   4, 246, 154, 276, 104,  22,  30],
       [187, 104,   2,   7,  26,  86,  26,  32, 344, 186],
       [ 38, 291,   0,   5,  14,  37, 133,  44,  83, 355]], dtype=int32)>






CNN with regularization (2 layers)

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Epoch 1/200
92/92 [==============================] - 3s 31ms/step - loss: 2.0114 - accuracy: 0.3543 - val_loss: 1.5445 - val_accuracy: 0.4857
Epoch 2/200
92/92 [==============================] - 3s 30ms/step - loss: 1.4754 - accuracy: 0.5053 - val_loss: 1.3696 - val_accuracy: 0.5493
Epoch 3/200
92/92 [==============================] - 3s 30ms/step - loss: 1.3389 - accuracy: 0.5574 - val_loss: 1.2256 - val_accuracy: 0.6003
Epoch 4/200
92/92 [==============================] - 3s 30ms/step - loss: 1.2488 - accuracy: 0.5941 - val_loss: 1.1592 - val_accuracy: 0.6387
Epoch 5/200
92/92 [==============================] - 3s 29ms/step - loss: 1.1855 - accuracy: 0.6210 - val_loss: 1.0885 - val_accuracy: 0.6717
Epoch 6/200
92/92 [==============================] - 3s 29ms/step - loss: 1.1324 - accuracy: 0.6416 - val_loss: 1.0651 - val_accuracy: 0.6763
Epoch 7/200
92/92 [==============================] - 3s 29ms/step - loss: 1.0956 - accuracy: 0.6581 - val_loss: 1.0326 - val_accuracy: 0.6897
Epoch 8/200
92/92 [==============================] - 3s 29ms/step - loss: 1.0561 - accuracy: 0.6720 - val_loss: 1.0126 - val_accuracy: 0.6877
Epoch 9/200
92/92 [==============================] - 3s 29ms/step - loss: 1.0298 - accuracy: 0.6826 - val_loss: 0.9826 - val_accuracy: 0.7030
Epoch 10/200
92/92 [==============================] - 3s 30ms/step - loss: 0.9928 - accuracy: 0.6994 - val_loss: 0.9896 - val_accuracy: 0.7100
Epoch 11/200
92/92 [==============================] - 3s 29ms/step - loss: 0.9722 - accuracy: 0.7067 - val_loss: 0.9655 - val_accuracy: 0.7120
Epoch 12/200
92/92 [==============================] - 3s 30ms/step - loss: 0.9494 - accuracy: 0.7162 - val_loss: 0.9796 - val_accuracy: 0.7017
Epoch 13/200
92/92 [==============================] - 3s 30ms/step - loss: 0.9393 - accuracy: 0.7210 - val_loss: 0.9313 - val_accuracy: 0.7287
Epoch 14/200
92/92 [==============================] - 3s 30ms/step - loss: 0.9069 - accuracy: 0.7355 - val_loss: 0.9261 - val_accuracy: 0.7333
Epoch 15/200
92/92 [==============================] - 3s 29ms/step - loss: 0.8859 - accuracy: 0.7434 - val_loss: 0.9281 - val_accuracy: 0.7360
Epoch 16/200
92/92 [==============================] - 3s 29ms/step - loss: 0.8754 - accuracy: 0.7487 - val_loss: 0.9134 - val_accuracy: 0.7413
Epoch 17/200
92/92 [==============================] - 3s 30ms/step - loss: 0.8523 - accuracy: 0.7604 - val_loss: 0.8914 - val_accuracy: 0.7477
Epoch 18/200
92/92 [==============================] - 3s 30ms/step - loss: 0.8350 - accuracy: 0.7673 - val_loss: 0.9318 - val_accuracy: 0.7400
Epoch 19/200
92/92 [==============================] - 3s 29ms/step - loss: 0.8217 - accuracy: 0.7730 - val_loss: 0.8972 - val_accuracy: 0.7523
Epoch 20/200
92/92 [==============================] - 3s 30ms/step - loss: 0.8138 - accuracy: 0.7785 - val_loss: 0.8914 - val_accuracy: 0.7617
Epoch 21/200
92/92 [==============================] - 3s 30ms/step - loss: 0.7950 - accuracy: 0.7882 - val_loss: 0.8986 - val_accuracy: 0.7573
Epoch 22/200
92/92 [==============================] - 3s 29ms/step - loss: 0.7844 - accuracy: 0.7931 - val_loss: 0.9098 - val_accuracy: 0.7550
Epoch 23/200
92/92 [==============================] - 3s 29ms/step - loss: 0.7663 - accuracy: 0.8022 - val_loss: 0.8903 - val_accuracy: 0.7627
Epoch 24/200
92/92 [==============================] - 3s 29ms/step - loss: 0.7515 - accuracy: 0.8082 - val_loss: 0.9075 - val_accuracy: 0.7503
Epoch 25/200
92/92 [==============================] - 3s 29ms/step - loss: 0.7403 - accuracy: 0.8128 - val_loss: 0.9215 - val_accuracy: 0.7533
Epoch 26/200
92/92 [==============================] - 3s 30ms/step - loss: 0.7284 - accuracy: 0.8205 - val_loss: 0.9045 - val_accuracy: 0.7630
Epoch 27/200
92/92 [==============================] - 3s 30ms/step - loss: 0.7237 - accuracy: 0.8209 - val_loss: 0.9108 - val_accuracy: 0.7700
Epoch 28/200
92/92 [==============================] - 3s 30ms/step - loss: 0.7086 - accuracy: 0.8288 - val_loss: 0.9184 - val_accuracy: 0.7640
Epoch 29/200
92/92 [==============================] - 3s 29ms/step - loss: 0.6991 - accuracy: 0.8332 - val_loss: 0.9245 - val_accuracy: 0.7633
Epoch 30/200
92/92 [==============================] - 3s 29ms/step - loss: 0.6873 - accuracy: 0.8382 - val_loss: 0.9164 - val_accuracy: 0.7733
Epoch 31/200
92/92 [==============================] - 3s 29ms/step - loss: 0.6741 - accuracy: 0.8440 - val_loss: 0.9158 - val_accuracy: 0.7710
Epoch 32/200
92/92 [==============================] - 3s 29ms/step - loss: 0.6733 - accuracy: 0.8454 - val_loss: 0.9595 - val_accuracy: 0.7580
Epoch 33/200
92/92 [==============================] - 3s 29ms/step - loss: 0.6609 - accuracy: 0.8507 - val_loss: 0.9326 - val_accuracy: 0.7727

Model: "sequential_9"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_5 (Conv2D)            (None, 30, 30, 128)       3584      
_________________________________________________________________
max_pooling2d_5 (MaxPooling2 (None, 15, 15, 128)       0         
_________________________________________________________________
dropout_5 (Dropout)          (None, 15, 15, 128)       0         
_________________________________________________________________
conv2d_6 (Conv2D)            (None, 13, 13, 256)       295168    
_________________________________________________________________
max_pooling2d_6 (MaxPooling2 (None, 6, 6, 256)         0         
_________________________________________________________________
dropout_6 (Dropout)          (None, 6, 6, 256)         0         
_________________________________________________________________
flatten_9 (Flatten)          (None, 9216)              0         
_________________________________________________________________
dense_26 (Dense)             (None, 384)               3539328   
_________________________________________________________________
dense_27 (Dense)             (None, 10)                3850      
=================================================================
Total params: 3,841,930
Trainable params: 3,841,930
Non-trainable params: 0
_________________________________________________________________

313/313 [==============================] - 1s 3ms/step - loss: 0.9610 - accuracy: 0.7601
test set accuracy:  76.010000705719

shape of preds:  (10000, 10)

dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])





<tf.Tensor: shape=(10, 10), dtype=int32, numpy=
array([[785,  19,  42,  14,  14,   6,   9,   9,  59,  43],
       [  9, 871,  10,   3,   2,   4,  12,   0,  18,  71],
       [ 53,   4, 643,  63,  80,  50,  51,  25,  18,  13],
       [ 16,  12,  59, 597,  43, 151,  48,  39,  14,  21],
       [ 14,   2,  59,  68, 679,  31,  53,  75,  13,   6],
       [ 10,   4,  48, 165,  29, 661,  18,  44,  10,  11],
       [  5,   2,  39,  64,  17,  20, 834,   6,   7,   6],
       [ 11,   3,  23,  32,  22,  52,   5, 831,   3,  18],
       [ 40,  33,  16,  12,   0,   3,   2,   5, 860,  29],
       [ 17,  67,  11,  14,   3,   4,   4,  11,  29, 840]], dtype=int32)>






CNN with Regularization (3 layers)

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Epoch 1/200
92/92 [==============================] - 4s 38ms/step - loss: 2.1653 - accuracy: 0.2835 - val_loss: 1.7422 - val_accuracy: 0.3990
Epoch 2/200
92/92 [==============================] - 3s 36ms/step - loss: 1.5890 - accuracy: 0.4508 - val_loss: 1.4349 - val_accuracy: 0.5130
Epoch 3/200
92/92 [==============================] - 3s 35ms/step - loss: 1.3924 - accuracy: 0.5273 - val_loss: 1.2754 - val_accuracy: 0.5750
Epoch 4/200
92/92 [==============================] - 3s 36ms/step - loss: 1.2462 - accuracy: 0.5846 - val_loss: 1.1474 - val_accuracy: 0.6300
Epoch 5/200
92/92 [==============================] - 3s 36ms/step - loss: 1.1548 - accuracy: 0.6152 - val_loss: 1.0085 - val_accuracy: 0.6760
Epoch 6/200
92/92 [==============================] - 3s 35ms/step - loss: 1.0776 - accuracy: 0.6460 - val_loss: 1.0275 - val_accuracy: 0.6600
Epoch 7/200
92/92 [==============================] - 3s 35ms/step - loss: 1.0209 - accuracy: 0.6644 - val_loss: 0.9254 - val_accuracy: 0.6937
Epoch 8/200
92/92 [==============================] - 3s 35ms/step - loss: 0.9776 - accuracy: 0.6797 - val_loss: 0.9042 - val_accuracy: 0.7160
Epoch 9/200
92/92 [==============================] - 3s 35ms/step - loss: 0.9360 - accuracy: 0.6963 - val_loss: 0.8459 - val_accuracy: 0.7357
Epoch 10/200
92/92 [==============================] - 3s 35ms/step - loss: 0.9070 - accuracy: 0.7081 - val_loss: 0.8283 - val_accuracy: 0.7427
Epoch 11/200
92/92 [==============================] - 3s 36ms/step - loss: 0.8700 - accuracy: 0.7199 - val_loss: 0.7990 - val_accuracy: 0.7473
Epoch 12/200
92/92 [==============================] - 3s 36ms/step - loss: 0.8355 - accuracy: 0.7304 - val_loss: 0.7741 - val_accuracy: 0.7467
Epoch 13/200
92/92 [==============================] - 3s 36ms/step - loss: 0.8122 - accuracy: 0.7397 - val_loss: 0.7699 - val_accuracy: 0.7587
Epoch 14/200
92/92 [==============================] - 3s 36ms/step - loss: 0.7918 - accuracy: 0.7477 - val_loss: 0.7825 - val_accuracy: 0.7553
Epoch 15/200
92/92 [==============================] - 3s 35ms/step - loss: 0.7675 - accuracy: 0.7569 - val_loss: 0.7604 - val_accuracy: 0.7633
Epoch 16/200
92/92 [==============================] - 3s 36ms/step - loss: 0.7440 - accuracy: 0.7642 - val_loss: 0.7260 - val_accuracy: 0.7807
Epoch 17/200
92/92 [==============================] - 3s 36ms/step - loss: 0.7288 - accuracy: 0.7721 - val_loss: 0.7135 - val_accuracy: 0.7807
Epoch 18/200
92/92 [==============================] - 3s 36ms/step - loss: 0.7146 - accuracy: 0.7744 - val_loss: 0.7225 - val_accuracy: 0.7743
Epoch 19/200
92/92 [==============================] - 3s 35ms/step - loss: 0.6969 - accuracy: 0.7811 - val_loss: 0.6953 - val_accuracy: 0.7877
Epoch 20/200
92/92 [==============================] - 3s 35ms/step - loss: 0.6835 - accuracy: 0.7874 - val_loss: 0.6939 - val_accuracy: 0.7827
Epoch 21/200
92/92 [==============================] - 3s 35ms/step - loss: 0.6667 - accuracy: 0.7931 - val_loss: 0.6873 - val_accuracy: 0.7893
Epoch 22/200
92/92 [==============================] - 3s 36ms/step - loss: 0.6547 - accuracy: 0.7973 - val_loss: 0.6840 - val_accuracy: 0.7917
Epoch 23/200
92/92 [==============================] - 3s 36ms/step - loss: 0.6380 - accuracy: 0.8032 - val_loss: 0.7018 - val_accuracy: 0.7803
Epoch 24/200
92/92 [==============================] - 3s 36ms/step - loss: 0.6299 - accuracy: 0.8070 - val_loss: 0.6658 - val_accuracy: 0.7957
Epoch 25/200
92/92 [==============================] - 3s 36ms/step - loss: 0.6181 - accuracy: 0.8104 - val_loss: 0.6596 - val_accuracy: 0.8040
Epoch 26/200
92/92 [==============================] - 3s 35ms/step - loss: 0.6038 - accuracy: 0.8161 - val_loss: 0.6617 - val_accuracy: 0.7993
Epoch 27/200
92/92 [==============================] - 3s 36ms/step - loss: 0.5939 - accuracy: 0.8198 - val_loss: 0.6499 - val_accuracy: 0.8050
Epoch 28/200
92/92 [==============================] - 3s 36ms/step - loss: 0.5873 - accuracy: 0.8219 - val_loss: 0.6627 - val_accuracy: 0.8033
Epoch 29/200
92/92 [==============================] - 3s 36ms/step - loss: 0.5738 - accuracy: 0.8289 - val_loss: 0.6629 - val_accuracy: 0.7940
Epoch 30/200
92/92 [==============================] - 3s 36ms/step - loss: 0.5633 - accuracy: 0.8316 - val_loss: 0.6451 - val_accuracy: 0.8047
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Model: "sequential_10"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_7 (Conv2D)            (None, 30, 30, 128)       3584      
_________________________________________________________________
max_pooling2d_7 (MaxPooling2 (None, 15, 15, 128)       0         
_________________________________________________________________
dropout_7 (Dropout)          (None, 15, 15, 128)       0         
_________________________________________________________________
conv2d_8 (Conv2D)            (None, 13, 13, 256)       295168    
_________________________________________________________________
max_pooling2d_8 (MaxPooling2 (None, 6, 6, 256)         0         
_________________________________________________________________
dropout_8 (Dropout)          (None, 6, 6, 256)         0         
_________________________________________________________________
conv2d_9 (Conv2D)            (None, 4, 4, 512)         1180160   
_________________________________________________________________
max_pooling2d_9 (MaxPooling2 (None, 2, 2, 512)         0         
_________________________________________________________________
dropout_9 (Dropout)          (None, 2, 2, 512)         0         
_________________________________________________________________
flatten_10 (Flatten)         (None, 2048)              0         
_________________________________________________________________
dense_28 (Dense)             (None, 384)               786816    
_________________________________________________________________
dense_29 (Dense)             (None, 10)                3850      
=================================================================
Total params: 2,269,578
Trainable params: 2,269,578
Non-trainable params: 0
_________________________________________________________________

313/313 [==============================] - 1s 3ms/step - loss: 0.6843 - accuracy: 0.7980
test set accuracy:  79.79999780654907
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shape of preds:  (10000, 10)

dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])




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<tf.Tensor: shape=(10, 10), dtype=int32, numpy=
array([[834,  23,  17,  18,   9,   1,   7,  11,  52,  28],
       [  8, 924,   2,   6,   0,   1,   4,   5,  15,  35],
       [ 58,   5, 674,  60,  63,  37,  55,  29,  13,   6],
       [ 12,   6,  51, 715,  40,  94,  38,  26,   8,  10],
       [ 13,   2,  49,  61, 750,  23,  36,  54,  10,   2],
       [ 12,   4,  32, 190,  34, 670,  12,  40,   2,   4],
       [  4,   4,  31,  55,  22,  13, 859,   6,   5,   1],
       [  8,   1,  24,  38,  24,  43,   4, 848,   1,   9],
       [ 45,  21,   7,  15,   1,   3,   6,   6, 877,  19],
       [ 15,  82,   6,  19,   1,   4,   6,  15,  23, 829]], dtype=int32)>

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CNN with alternative Regularizer (3 layers)

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Epoch 1/200
92/92 [==============================] - 4s 37ms/step - loss: 6.8962 - accuracy: 0.2987 - val_loss: 1.7306 - val_accuracy: 0.3950
Epoch 2/200
92/92 [==============================] - 3s 36ms/step - loss: 1.6391 - accuracy: 0.4360 - val_loss: 1.5276 - val_accuracy: 0.4837
Epoch 3/200
92/92 [==============================] - 3s 35ms/step - loss: 1.4891 - accuracy: 0.5018 - val_loss: 1.4275 - val_accuracy: 0.5337
Epoch 4/200
92/92 [==============================] - 3s 36ms/step - loss: 1.4186 - accuracy: 0.5313 - val_loss: 1.3244 - val_accuracy: 0.5570
Epoch 5/200
92/92 [==============================] - 3s 36ms/step - loss: 1.3668 - accuracy: 0.5544 - val_loss: 1.2201 - val_accuracy: 0.6197
Epoch 6/200
92/92 [==============================] - 3s 36ms/step - loss: 1.3126 - accuracy: 0.5783 - val_loss: 1.2502 - val_accuracy: 0.5940
Epoch 7/200
92/92 [==============================] - 3s 36ms/step - loss: 1.2706 - accuracy: 0.5955 - val_loss: 1.2089 - val_accuracy: 0.6067
Epoch 8/200
92/92 [==============================] - 3s 36ms/step - loss: 1.2294 - accuracy: 0.6117 - val_loss: 1.1611 - val_accuracy: 0.6470
Epoch 9/200
92/92 [==============================] - 3s 36ms/step - loss: 1.1932 - accuracy: 0.6254 - val_loss: 1.1007 - val_accuracy: 0.6553
Epoch 10/200
92/92 [==============================] - 3s 36ms/step - loss: 1.1526 - accuracy: 0.6402 - val_loss: 1.0495 - val_accuracy: 0.6893
Epoch 11/200
92/92 [==============================] - 3s 36ms/step - loss: 1.1274 - accuracy: 0.6514 - val_loss: 1.0755 - val_accuracy: 0.6757
Epoch 12/200
92/92 [==============================] - 3s 35ms/step - loss: 1.1080 - accuracy: 0.6588 - val_loss: 1.0038 - val_accuracy: 0.7027
Epoch 13/200
92/92 [==============================] - 3s 36ms/step - loss: 1.0764 - accuracy: 0.6712 - val_loss: 0.9993 - val_accuracy: 0.7003
Epoch 14/200
92/92 [==============================] - 3s 36ms/step - loss: 1.0550 - accuracy: 0.6814 - val_loss: 1.0614 - val_accuracy: 0.6733
Epoch 15/200
92/92 [==============================] - 3s 35ms/step - loss: 1.0522 - accuracy: 0.6816 - val_loss: 0.9490 - val_accuracy: 0.7213
Epoch 16/200
92/92 [==============================] - 3s 35ms/step - loss: 1.0081 - accuracy: 0.6964 - val_loss: 0.9984 - val_accuracy: 0.6967
Epoch 17/200
92/92 [==============================] - 3s 36ms/step - loss: 0.9928 - accuracy: 0.7044 - val_loss: 0.9711 - val_accuracy: 0.7067
Epoch 18/200
92/92 [==============================] - 3s 36ms/step - loss: 0.9773 - accuracy: 0.7103 - val_loss: 0.9083 - val_accuracy: 0.7393
Epoch 19/200
92/92 [==============================] - 3s 36ms/step - loss: 0.9530 - accuracy: 0.7211 - val_loss: 0.8929 - val_accuracy: 0.7450
Epoch 20/200
92/92 [==============================] - 3s 35ms/step - loss: 0.9381 - accuracy: 0.7259 - val_loss: 0.8940 - val_accuracy: 0.7427
Epoch 21/200
92/92 [==============================] - 3s 36ms/step - loss: 0.9307 - accuracy: 0.7266 - val_loss: 0.8848 - val_accuracy: 0.7540
Epoch 22/200
92/92 [==============================] - 3s 35ms/step - loss: 0.9058 - accuracy: 0.7354 - val_loss: 0.8565 - val_accuracy: 0.7553
Epoch 23/200
92/92 [==============================] - 3s 35ms/step - loss: 0.8940 - accuracy: 0.7426 - val_loss: 0.8366 - val_accuracy: 0.7640
Epoch 24/200
92/92 [==============================] - 3s 35ms/step - loss: 0.8886 - accuracy: 0.7446 - val_loss: 0.8728 - val_accuracy: 0.7487
Epoch 25/200
92/92 [==============================] - 3s 36ms/step - loss: 0.8702 - accuracy: 0.7506 - val_loss: 0.8382 - val_accuracy: 0.7680
Epoch 26/200
92/92 [==============================] - 3s 35ms/step - loss: 0.8599 - accuracy: 0.7529 - val_loss: 0.8365 - val_accuracy: 0.7597
Epoch 27/200
92/92 [==============================] - 3s 36ms/step - loss: 0.8494 - accuracy: 0.7588 - val_loss: 0.8582 - val_accuracy: 0.7617
Epoch 28/200
92/92 [==============================] - 3s 35ms/step - loss: 0.8304 - accuracy: 0.7647 - val_loss: 0.8196 - val_accuracy: 0.7693
Epoch 29/200
92/92 [==============================] - 3s 36ms/step - loss: 0.8183 - accuracy: 0.7700 - val_loss: 0.8263 - val_accuracy: 0.7677
Epoch 30/200
92/92 [==============================] - 3s 35ms/step - loss: 0.8117 - accuracy: 0.7723 - val_loss: 0.7969 - val_accuracy: 0.7810
Epoch 31/200
92/92 [==============================] - 3s 35ms/step - loss: 0.8039 - accuracy: 0.7763 - val_loss: 0.7898 - val_accuracy: 0.7883
Epoch 32/200
92/92 [==============================] - 3s 35ms/step - loss: 0.7902 - accuracy: 0.7781 - val_loss: 0.7903 - val_accuracy: 0.7783
Epoch 33/200
92/92 [==============================] - 3s 36ms/step - loss: 0.7805 - accuracy: 0.7823 - val_loss: 0.7962 - val_accuracy: 0.7727
Epoch 34/200
92/92 [==============================] - 3s 35ms/step - loss: 0.7832 - accuracy: 0.7812 - val_loss: 0.7624 - val_accuracy: 0.7963
Epoch 35/200
92/92 [==============================] - 3s 36ms/step - loss: 0.7575 - accuracy: 0.7910 - val_loss: 0.7626 - val_accuracy: 0.7973
Epoch 36/200
92/92 [==============================] - 3s 38ms/step - loss: 0.7509 - accuracy: 0.7920 - val_loss: 0.7639 - val_accuracy: 0.7903
Epoch 37/200
92/92 [==============================] - 3s 35ms/step - loss: 0.7361 - accuracy: 0.7983 - val_loss: 0.7524 - val_accuracy: 0.7947
Epoch 38/200
92/92 [==============================] - 3s 35ms/step - loss: 0.7315 - accuracy: 0.8008 - val_loss: 0.7697 - val_accuracy: 0.7920
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Model: "sequential_11"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_10 (Conv2D)           (None, 30, 30, 128)       3584      
_________________________________________________________________
max_pooling2d_10 (MaxPooling (None, 15, 15, 128)       0         
_________________________________________________________________
dropout_10 (Dropout)         (None, 15, 15, 128)       0         
_________________________________________________________________
conv2d_11 (Conv2D)           (None, 13, 13, 256)       295168    
_________________________________________________________________
max_pooling2d_11 (MaxPooling (None, 6, 6, 256)         0         
_________________________________________________________________
dropout_11 (Dropout)         (None, 6, 6, 256)         0         
_________________________________________________________________
conv2d_12 (Conv2D)           (None, 4, 4, 512)         1180160   
_________________________________________________________________
max_pooling2d_12 (MaxPooling (None, 2, 2, 512)         0         
_________________________________________________________________
dropout_12 (Dropout)         (None, 2, 2, 512)         0         
_________________________________________________________________
flatten_11 (Flatten)         (None, 2048)              0         
_________________________________________________________________
dense_30 (Dense)             (None, 384)               786816    
_________________________________________________________________
dense_31 (Dense)             (None, 10)                3850      
=================================================================
Total params: 2,269,578
Trainable params: 2,269,578
Non-trainable params: 0
_________________________________________________________________

313/313 [==============================] - 1s 3ms/step - loss: 0.8008 - accuracy: 0.7803
test set accuracy:  78.03000211715698

shape of preds:  (10000, 10)

dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])



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<tf.Tensor: shape=(10, 10), dtype=int32, numpy=
array([[830,   7,  24,  23,   9,   3,  14,  12,  59,  19],
       [ 18, 874,   2,   8,   0,   1,  19,   6,  29,  43],
       [ 63,   4, 601,  91,  55,  58,  91,  28,   6,   3],
       [ 10,   1,  38, 699,  32, 115,  68,  24,   8,   5],
       [ 24,   1,  38,  78, 700,  29,  61,  60,   8,   1],
       [ 11,   0,  23, 197,  28, 674,  15,  48,   2,   2],
       [  5,   1,  20,  63,  12,  13, 876,   6,   3,   1],
       [ 13,   0,  12,  38,  36,  54,   6, 834,   1,   6],
       [ 37,  13,   4,  16,   0,   5,   9,   5, 894,  17],
       [ 24,  58,   9,  13,   3,   8,  17,  20,  27, 821]], dtype=int32)>

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Improving Best Model P1

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Epoch 1/200
735/735 [==============================] - 12s 15ms/step - loss: 1.6680 - accuracy: 0.4314 - val_loss: 1.2758 - val_accuracy: 0.5880
Epoch 2/200
735/735 [==============================] - 11s 15ms/step - loss: 1.2714 - accuracy: 0.5807 - val_loss: 1.1330 - val_accuracy: 0.6343
Epoch 3/200
735/735 [==============================] - 11s 15ms/step - loss: 1.1372 - accuracy: 0.6328 - val_loss: 1.0038 - val_accuracy: 0.6810
Epoch 4/200
735/735 [==============================] - 11s 15ms/step - loss: 1.0508 - accuracy: 0.6704 - val_loss: 0.9593 - val_accuracy: 0.7070
Epoch 5/200
735/735 [==============================] - 11s 15ms/step - loss: 0.9928 - accuracy: 0.6915 - val_loss: 0.9160 - val_accuracy: 0.7277
Epoch 6/200
735/735 [==============================] - 11s 15ms/step - loss: 0.9448 - accuracy: 0.7073 - val_loss: 0.8695 - val_accuracy: 0.7450
Epoch 7/200
735/735 [==============================] - 11s 15ms/step - loss: 0.8959 - accuracy: 0.7269 - val_loss: 0.8757 - val_accuracy: 0.7433
Epoch 8/200
735/735 [==============================] - 11s 15ms/step - loss: 0.8650 - accuracy: 0.7395 - val_loss: 0.7948 - val_accuracy: 0.7677
Epoch 9/200
735/735 [==============================] - 11s 15ms/step - loss: 0.8379 - accuracy: 0.7475 - val_loss: 0.8008 - val_accuracy: 0.7677
Epoch 10/200
735/735 [==============================] - 11s 15ms/step - loss: 0.8063 - accuracy: 0.7592 - val_loss: 0.7892 - val_accuracy: 0.7670
Epoch 11/200
735/735 [==============================] - 11s 15ms/step - loss: 0.7857 - accuracy: 0.7678 - val_loss: 0.7862 - val_accuracy: 0.7683
Epoch 12/200
735/735 [==============================] - 11s 15ms/step - loss: 0.7610 - accuracy: 0.7770 - val_loss: 0.7505 - val_accuracy: 0.7750
Epoch 13/200
735/735 [==============================] - 11s 15ms/step - loss: 0.7431 - accuracy: 0.7821 - val_loss: 0.7366 - val_accuracy: 0.7880
Epoch 14/200
735/735 [==============================] - 11s 15ms/step - loss: 0.7317 - accuracy: 0.7857 - val_loss: 0.7270 - val_accuracy: 0.7873
Epoch 15/200
735/735 [==============================] - 11s 15ms/step - loss: 0.7082 - accuracy: 0.7941 - val_loss: 0.7506 - val_accuracy: 0.7837
Epoch 16/200
735/735 [==============================] - 11s 15ms/step - loss: 0.6990 - accuracy: 0.7971 - val_loss: 0.7391 - val_accuracy: 0.7897
Epoch 17/200
735/735 [==============================] - 11s 15ms/step - loss: 0.6845 - accuracy: 0.8011 - val_loss: 0.7354 - val_accuracy: 0.7863
Epoch 18/200
735/735 [==============================] - 11s 15ms/step - loss: 0.6665 - accuracy: 0.8076 - val_loss: 0.7036 - val_accuracy: 0.8040
Epoch 19/200
735/735 [==============================] - 11s 15ms/step - loss: 0.6677 - accuracy: 0.8069 - val_loss: 0.7061 - val_accuracy: 0.7990
Epoch 20/200
735/735 [==============================] - 11s 15ms/step - loss: 0.6449 - accuracy: 0.8144 - val_loss: 0.7050 - val_accuracy: 0.8017
Epoch 21/200
735/735 [==============================] - 11s 15ms/step - loss: 0.6296 - accuracy: 0.8198 - val_loss: 0.6981 - val_accuracy: 0.8053
Epoch 22/200
735/735 [==============================] - 11s 15ms/step - loss: 0.6174 - accuracy: 0.8249 - val_loss: 0.7008 - val_accuracy: 0.8053
Epoch 23/200
735/735 [==============================] - 11s 15ms/step - loss: 0.6102 - accuracy: 0.8256 - val_loss: 0.6932 - val_accuracy: 0.8053
Epoch 24/200
735/735 [==============================] - 11s 15ms/step - loss: 0.6067 - accuracy: 0.8270 - val_loss: 0.7146 - val_accuracy: 0.7940
Epoch 25/200
735/735 [==============================] - 11s 15ms/step - loss: 0.5978 - accuracy: 0.8293 - val_loss: 0.6727 - val_accuracy: 0.8063
Epoch 26/200
735/735 [==============================] - 11s 15ms/step - loss: 0.5834 - accuracy: 0.8336 - val_loss: 0.7032 - val_accuracy: 0.7987
Epoch 27/200
735/735 [==============================] - 11s 15ms/step - loss: 0.5777 - accuracy: 0.8339 - val_loss: 0.6653 - val_accuracy: 0.8153
Epoch 28/200
735/735 [==============================] - 11s 15ms/step - loss: 0.5636 - accuracy: 0.8413 - val_loss: 0.6716 - val_accuracy: 0.8023
Epoch 29/200
735/735 [==============================] - 11s 15ms/step - loss: 0.5640 - accuracy: 0.8403 - val_loss: 0.6821 - val_accuracy: 0.8087
Epoch 30/200
735/735 [==============================] - 11s 15ms/step - loss: 0.5605 - accuracy: 0.8388 - val_loss: 0.6686 - val_accuracy: 0.8157
Epoch 31/200
735/735 [==============================] - 11s 15ms/step - loss: 0.5471 - accuracy: 0.8447 - val_loss: 0.6637 - val_accuracy: 0.8163
Epoch 32/200
735/735 [==============================] - 11s 15ms/step - loss: 0.5465 - accuracy: 0.8451 - val_loss: 0.6538 - val_accuracy: 0.8103
Epoch 33/200
735/735 [==============================] - 11s 15ms/step - loss: 0.5334 - accuracy: 0.8490 - val_loss: 0.6667 - val_accuracy: 0.8030
Epoch 34/200
735/735 [==============================] - 11s 15ms/step - loss: 0.5307 - accuracy: 0.8482 - val_loss: 0.7031 - val_accuracy: 0.7993
Epoch 35/200
735/735 [==============================] - 11s 15ms/step - loss: 0.5199 - accuracy: 0.8522 - val_loss: 0.6531 - val_accuracy: 0.8160
Epoch 36/200
735/735 [==============================] - 11s 15ms/step - loss: 0.5162 - accuracy: 0.8540 - val_loss: 0.6619 - val_accuracy: 0.8080
Epoch 37/200
735/735 [==============================] - 11s 15ms/step - loss: 0.5117 - accuracy: 0.8559 - val_loss: 0.6616 - val_accuracy: 0.8103
Epoch 38/200
735/735 [==============================] - 11s 15ms/step - loss: 0.4991 - accuracy: 0.8584 - val_loss: 0.6641 - val_accuracy: 0.8083
Epoch 39/200
735/735 [==============================] - 11s 15ms/step - loss: 0.5006 - accuracy: 0.8596 - val_loss: 0.6485 - val_accuracy: 0.8177
Epoch 40/200
735/735 [==============================] - 11s 15ms/step - loss: 0.4935 - accuracy: 0.8626 - val_loss: 0.6577 - val_accuracy: 0.8133
Epoch 41/200
735/735 [==============================] - 11s 15ms/step - loss: 0.4946 - accuracy: 0.8620 - val_loss: 0.6370 - val_accuracy: 0.8137
Epoch 42/200
735/735 [==============================] - 11s 15ms/step - loss: 0.4829 - accuracy: 0.8641 - val_loss: 0.6500 - val_accuracy: 0.8097
Epoch 43/200
735/735 [==============================] - 11s 15ms/step - loss: 0.4777 - accuracy: 0.8663 - val_loss: 0.6801 - val_accuracy: 0.8073
Epoch 44/200
735/735 [==============================] - 11s 15ms/step - loss: 0.4818 - accuracy: 0.8638 - val_loss: 0.6777 - val_accuracy: 0.8050
Epoch 45/200
735/735 [==============================] - 11s 15ms/step - loss: 0.4737 - accuracy: 0.8648 - val_loss: 0.6547 - val_accuracy: 0.8127
Epoch 46/200
735/735 [==============================] - 11s 15ms/step - loss: 0.4630 - accuracy: 0.8686 - val_loss: 0.6294 - val_accuracy: 0.8210
Epoch 47/200
735/735 [==============================] - 11s 15ms/step - loss: 0.4618 - accuracy: 0.8680 - val_loss: 0.6252 - val_accuracy: 0.8213
Epoch 48/200
735/735 [==============================] - 11s 15ms/step - loss: 0.4537 - accuracy: 0.8725 - val_loss: 0.6488 - val_accuracy: 0.8137
Epoch 49/200
735/735 [==============================] - 11s 15ms/step - loss: 0.4541 - accuracy: 0.8724 - val_loss: 0.6350 - val_accuracy: 0.8133
Epoch 50/200
735/735 [==============================] - 11s 15ms/step - loss: 0.4488 - accuracy: 0.8736 - val_loss: 0.6326 - val_accuracy: 0.8233
Epoch 51/200
735/735 [==============================] - 11s 15ms/step - loss: 0.4528 - accuracy: 0.8720 - val_loss: 0.6371 - val_accuracy: 0.8197
Epoch 52/200
735/735 [==============================] - 11s 15ms/step - loss: 0.4444 - accuracy: 0.8734 - val_loss: 0.6311 - val_accuracy: 0.8203
Epoch 53/200
735/735 [==============================] - 11s 15ms/step - loss: 0.4368 - accuracy: 0.8765 - val_loss: 0.6286 - val_accuracy: 0.8247
Epoch 54/200
735/735 [==============================] - 11s 15ms/step - loss: 0.4380 - accuracy: 0.8760 - val_loss: 0.6409 - val_accuracy: 0.8177
Epoch 55/200
735/735 [==============================] - 11s 15ms/step - loss: 0.4246 - accuracy: 0.8808 - val_loss: 0.6451 - val_accuracy: 0.8157
Epoch 56/200
735/735 [==============================] - 11s 15ms/step - loss: 0.4214 - accuracy: 0.8815 - val_loss: 0.6735 - val_accuracy: 0.8083
Epoch 57/200
735/735 [==============================] - 11s 15ms/step - loss: 0.4247 - accuracy: 0.8790 - val_loss: 0.6157 - val_accuracy: 0.8230
Epoch 58/200
735/735 [==============================] - 11s 15ms/step - loss: 0.4153 - accuracy: 0.8816 - val_loss: 0.6321 - val_accuracy: 0.8247
Epoch 59/200
735/735 [==============================] - 11s 15ms/step - loss: 0.4182 - accuracy: 0.8818 - val_loss: 0.6255 - val_accuracy: 0.8157
Epoch 60/200
735/735 [==============================] - 11s 15ms/step - loss: 0.4161 - accuracy: 0.8834 - val_loss: 0.6361 - val_accuracy: 0.8157
Epoch 61/200
735/735 [==============================] - 11s 15ms/step - loss: 0.4031 - accuracy: 0.8867 - val_loss: 0.6485 - val_accuracy: 0.8167
Epoch 62/200
735/735 [==============================] - 11s 15ms/step - loss: 0.4073 - accuracy: 0.8846 - val_loss: 0.6351 - val_accuracy: 0.8180
Epoch 63/200
735/735 [==============================] - 11s 15ms/step - loss: 0.4052 - accuracy: 0.8848 - val_loss: 0.6439 - val_accuracy: 0.8093
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Model: "sequential_18"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_37 (Conv2D)           (None, 30, 30, 256)       7168      
_________________________________________________________________
max_pooling2d_31 (MaxPooling (None, 15, 15, 256)       0         
_________________________________________________________________
dropout_33 (Dropout)         (None, 15, 15, 256)       0         
_________________________________________________________________
conv2d_38 (Conv2D)           (None, 13, 13, 512)       1180160   
_________________________________________________________________
max_pooling2d_32 (MaxPooling (None, 6, 6, 512)         0         
_________________________________________________________________
dropout_34 (Dropout)         (None, 6, 6, 512)         0         
_________________________________________________________________
conv2d_39 (Conv2D)           (None, 4, 4, 640)         2949760   
_________________________________________________________________
max_pooling2d_33 (MaxPooling (None, 2, 2, 640)         0         
_________________________________________________________________
dropout_35 (Dropout)         (None, 2, 2, 640)         0         
_________________________________________________________________
flatten_18 (Flatten)         (None, 2560)              0         
_________________________________________________________________
dense_44 (Dense)             (None, 480)               1229280   
_________________________________________________________________
dense_45 (Dense)             (None, 10)                4810      
=================================================================
Total params: 5,371,178
Trainable params: 5,371,178
Non-trainable params: 0
_________________________________________________________________
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313/313 [==============================] - 1s 4ms/step - loss: 0.6893 - accuracy: 0.8014
test set accuracy:  80.14000058174133
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shape of preds:  (10000, 10)

dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])
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<tf.Tensor: shape=(10, 10), dtype=int32, numpy=
array([[745,   8,  29,  20,  32,   5,   7,  11,  95,  48],
       [  8, 870,   3,   2,   1,   7,   5,   3,  23,  78],
       [ 58,   2, 668,  44,  99,  45,  39,  29,   8,   8],
       [ 12,   3,  42, 637,  88, 129,  34,  26,  12,  17],
       [  8,   1,  27,  33, 857,  23,  14,  28,   8,   1],
       [  7,   3,  23, 112,  60, 740,   9,  31,   7,   8],
       [  3,   0,  29,  50,  51,  15, 839,   5,   6,   2],
       [  7,   1,  14,  27,  63,  39,   1, 829,   3,  16],
       [ 14,   5,   4,   8,   4,   2,   1,   1, 928,  33],
       [  6,  27,   5,  13,   2,  10,   5,   3,  28, 901]], dtype=int32)>



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Model: "sequential_18"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_37 (Conv2D)           (None, 30, 30, 256)       7168      
_________________________________________________________________
max_pooling2d_31 (MaxPooling (None, 15, 15, 256)       0         
_________________________________________________________________
dropout_33 (Dropout)         (None, 15, 15, 256)       0         
_________________________________________________________________
conv2d_38 (Conv2D)           (None, 13, 13, 512)       1180160   
_________________________________________________________________
max_pooling2d_32 (MaxPooling (None, 6, 6, 512)         0         
_________________________________________________________________
dropout_34 (Dropout)         (None, 6, 6, 512)         0         
_________________________________________________________________
conv2d_39 (Conv2D)           (None, 4, 4, 640)         2949760   
_________________________________________________________________
max_pooling2d_33 (MaxPooling (None, 2, 2, 640)         0         
_________________________________________________________________
dropout_35 (Dropout)         (None, 2, 2, 640)         0         
_________________________________________________________________
flatten_18 (Flatten)         (None, 2560)              0         
_________________________________________________________________
dense_44 (Dense)             (None, 480)               1229280   
_________________________________________________________________
dense_45 (Dense)             (None, 10)                4810      
=================================================================
Total params: 5,371,178
Trainable params: 5,371,178
Non-trainable params: 0
_________________________________________________________________
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313/313 [==============================] - 1s 4ms/step - loss: 0.6893 - accuracy: 0.8014
test set accuracy:  80.14000058174133
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shape of preds:  (10000, 10)
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dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])
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<tf.Tensor: shape=(10, 10), dtype=int32, numpy=
array([[745,   8,  29,  20,  32,   5,   7,  11,  95,  48],
       [  8, 870,   3,   2,   1,   7,   5,   3,  23,  78],
       [ 58,   2, 668,  44,  99,  45,  39,  29,   8,   8],
       [ 12,   3,  42, 637,  88, 129,  34,  26,  12,  17],
       [  8,   1,  27,  33, 857,  23,  14,  28,   8,   1],
       [  7,   3,  23, 112,  60, 740,   9,  31,   7,   8],
       [  3,   0,  29,  50,  51,  15, 839,   5,   6,   2],
       [  7,   1,  14,  27,  63,  39,   1, 829,   3,  16],
       [ 14,   5,   4,   8,   4,   2,   1,   1, 928,  33],
       [  6,  27,   5,  13,   2,  10,   5,   3,  28, 901]], dtype=int32)>

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8

['conv2d_40',
 'max_pooling2d_34',
 'dropout_36',
 'conv2d_41',
 'max_pooling2d_35',
 'dropout_37',
 'conv2d_42',
 'max_pooling2d_36',
 'dropout_38',
 'flatten_19',
 'dense_46',
 'dense_47']


Improving best model P2

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Epoch 1/200
92/92 [==============================] - 46s 447ms/step - loss: 3.3636 - accuracy: 0.2189 - val_loss: 2.2945 - val_accuracy: 0.3137
Epoch 2/200
92/92 [==============================] - 35s 381ms/step - loss: 1.9718 - accuracy: 0.3782 - val_loss: 1.7066 - val_accuracy: 0.4337
Epoch 3/200
92/92 [==============================] - 35s 382ms/step - loss: 1.6649 - accuracy: 0.4390 - val_loss: 1.5279 - val_accuracy: 0.4803
Epoch 4/200
92/92 [==============================] - 35s 382ms/step - loss: 1.5673 - accuracy: 0.4633 - val_loss: 1.4713 - val_accuracy: 0.4990
Epoch 5/200
92/92 [==============================] - 35s 382ms/step - loss: 1.4997 - accuracy: 0.4872 - val_loss: 1.4051 - val_accuracy: 0.5207
Epoch 6/200
92/92 [==============================] - 35s 382ms/step - loss: 1.4526 - accuracy: 0.5078 - val_loss: 1.4108 - val_accuracy: 0.5203
Epoch 7/200
92/92 [==============================] - 35s 382ms/step - loss: 1.4141 - accuracy: 0.5226 - val_loss: 1.4223 - val_accuracy: 0.5227
Epoch 8/200
92/92 [==============================] - 35s 382ms/step - loss: 1.3930 - accuracy: 0.5317 - val_loss: 1.3775 - val_accuracy: 0.5403
Epoch 9/200
92/92 [==============================] - 35s 383ms/step - loss: 1.3670 - accuracy: 0.5442 - val_loss: 1.3765 - val_accuracy: 0.5357
Epoch 10/200
92/92 [==============================] - 35s 383ms/step - loss: 1.3415 - accuracy: 0.5543 - val_loss: 1.3778 - val_accuracy: 0.5377
Epoch 11/200
92/92 [==============================] - 35s 382ms/step - loss: 1.3314 - accuracy: 0.5613 - val_loss: 1.3469 - val_accuracy: 0.5513
Epoch 12/200
92/92 [==============================] - 35s 383ms/step - loss: 1.3079 - accuracy: 0.5730 - val_loss: 1.3687 - val_accuracy: 0.5433
Epoch 13/200
92/92 [==============================] - 35s 382ms/step - loss: 1.2977 - accuracy: 0.5764 - val_loss: 1.3320 - val_accuracy: 0.5633
Epoch 14/200
92/92 [==============================] - 35s 383ms/step - loss: 1.2759 - accuracy: 0.5847 - val_loss: 1.3244 - val_accuracy: 0.5653
Epoch 15/200
92/92 [==============================] - 35s 382ms/step - loss: 1.2644 - accuracy: 0.5944 - val_loss: 1.3121 - val_accuracy: 0.5697
Epoch 16/200
92/92 [==============================] - 35s 383ms/step - loss: 1.2469 - accuracy: 0.6038 - val_loss: 1.3272 - val_accuracy: 0.5683
Epoch 17/200
92/92 [==============================] - 35s 383ms/step - loss: 1.2488 - accuracy: 0.5994 - val_loss: 1.3092 - val_accuracy: 0.5750
Epoch 18/200
92/92 [==============================] - 35s 383ms/step - loss: 1.2187 - accuracy: 0.6116 - val_loss: 1.3583 - val_accuracy: 0.5607
Epoch 19/200
92/92 [==============================] - 35s 383ms/step - loss: 1.2155 - accuracy: 0.6164 - val_loss: 1.3062 - val_accuracy: 0.5763
Epoch 20/200
92/92 [==============================] - 35s 383ms/step - loss: 1.1972 - accuracy: 0.6255 - val_loss: 1.3147 - val_accuracy: 0.5793
Epoch 21/200
92/92 [==============================] - 35s 383ms/step - loss: 1.1921 - accuracy: 0.6280 - val_loss: 1.4167 - val_accuracy: 0.5660
Epoch 22/200
92/92 [==============================] - 35s 383ms/step - loss: 1.1762 - accuracy: 0.6337 - val_loss: 1.3526 - val_accuracy: 0.5723
Epoch 23/200
92/92 [==============================] - 35s 382ms/step - loss: 1.1649 - accuracy: 0.6396 - val_loss: 1.3091 - val_accuracy: 0.5867
Epoch 24/200
92/92 [==============================] - 35s 383ms/step - loss: 1.1677 - accuracy: 0.6416 - val_loss: 1.3813 - val_accuracy: 0.5753
Epoch 25/200
92/92 [==============================] - 35s 383ms/step - loss: 1.1499 - accuracy: 0.6479 - val_loss: 1.3226 - val_accuracy: 0.5960
Epoch 26/200
92/92 [==============================] - 35s 382ms/step - loss: 1.1347 - accuracy: 0.6544 - val_loss: 1.3575 - val_accuracy: 0.5917
Epoch 27/200
92/92 [==============================] - 35s 382ms/step - loss: 1.1373 - accuracy: 0.6552 - val_loss: 1.3540 - val_accuracy: 0.5903
Epoch 28/200
92/92 [==============================] - 35s 383ms/step - loss: 1.1242 - accuracy: 0.6622 - val_loss: 1.3707 - val_accuracy: 0.5780
Epoch 29/200
92/92 [==============================] - 35s 383ms/step - loss: 1.1189 - accuracy: 0.6639 - val_loss: 1.3683 - val_accuracy: 0.5873
Epoch 30/200
92/92 [==============================] - 35s 382ms/step - loss: 1.1060 - accuracy: 0.6707 - val_loss: 1.3549 - val_accuracy: 0.5970
Epoch 31/200
92/92 [==============================] - 35s 383ms/step - loss: 1.0888 - accuracy: 0.6794 - val_loss: 1.3490 - val_accuracy: 0.6033
Epoch 32/200
92/92 [==============================] - 35s 383ms/step - loss: 1.0880 - accuracy: 0.6806 - val_loss: 1.3795 - val_accuracy: 0.5900
Epoch 33/200
92/92 [==============================] - 35s 383ms/step - loss: 1.0802 - accuracy: 0.6827 - val_loss: 1.4350 - val_accuracy: 0.5797
Epoch 34/200
92/92 [==============================] - 35s 383ms/step - loss: 1.0751 - accuracy: 0.6875 - val_loss: 1.3973 - val_accuracy: 0.5810
Epoch 35/200
92/92 [==============================] - 35s 382ms/step - loss: 1.0636 - accuracy: 0.6946 - val_loss: 1.4155 - val_accuracy: 0.5900
Epoch 36/200
92/92 [==============================] - 35s 382ms/step - loss: 1.0575 - accuracy: 0.6967 - val_loss: 1.4249 - val_accuracy: 0.5840
Epoch 37/200
92/92 [==============================] - 36s 386ms/step - loss: 1.0442 - accuracy: 0.6995 - val_loss: 1.4189 - val_accuracy: 0.5897
Epoch 38/200
92/92 [==============================] - 35s 383ms/step - loss: 1.0311 - accuracy: 0.7052 - val_loss: 1.4203 - val_accuracy: 0.5903
Epoch 39/200
92/92 [==============================] - 35s 382ms/step - loss: 1.0324 - accuracy: 0.7074 - val_loss: 1.4851 - val_accuracy: 0.5813
Epoch 40/200
92/92 [==============================] - 35s 383ms/step - loss: 1.0393 - accuracy: 0.7075 - val_loss: 1.4486 - val_accuracy: 0.5873
Epoch 41/200
92/92 [==============================] - 35s 383ms/step - loss: 1.0291 - accuracy: 0.7096 - val_loss: 1.4829 - val_accuracy: 0.5827
Code Text

Model: "sequential_14"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_22 (Conv2D)           (None, 31, 31, 128)       1664      
_________________________________________________________________
max_pooling2d_19 (MaxPooling (None, 31, 31, 128)       0         
_________________________________________________________________
dropout_20 (Dropout)         (None, 31, 31, 128)       0         
_________________________________________________________________
conv2d_23 (Conv2D)           (None, 30, 30, 256)       131328    
_________________________________________________________________
max_pooling2d_20 (MaxPooling (None, 30, 30, 256)       0         
_________________________________________________________________
dropout_21 (Dropout)         (None, 30, 30, 256)       0         
_________________________________________________________________
conv2d_24 (Conv2D)           (None, 29, 29, 512)       524800    
_________________________________________________________________
max_pooling2d_21 (MaxPooling (None, 29, 29, 512)       0         
_________________________________________________________________
dropout_22 (Dropout)         (None, 29, 29, 512)       0         
_________________________________________________________________
flatten_14 (Flatten)         (None, 430592)            0         
_________________________________________________________________
dense_36 (Dense)             (None, 384)               165347712 
_________________________________________________________________
dense_37 (Dense)             (None, 10)                3850      
=================================================================
Total params: 166,009,354
Trainable params: 166,009,354
Non-trainable params: 0
_________________________________________________________________
Code Text

313/313 [==============================] - 5s 14ms/step - loss: 1.5885 - accuracy: 0.5565
test set accuracy:  55.650001764297485
Code Text

shape of preds:  (10000, 10)
Code Text

dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])





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       [ 46,   6, 471, 103, 181,  42,  86,  36,  23,   6],
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       [ 14,   5,  51,  99, 129,  68,  34, 574,  10,  16],
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       [ 49,  90,  16,  94,  29,  23,  40,  32,  56, 571]], dtype=int32)>






Improving Best model P3

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Epoch 1/200
92/92 [==============================] - 13s 119ms/step - loss: 1.9704 - accuracy: 0.2837 - val_loss: 1.5952 - val_accuracy: 0.4187
Epoch 2/200
92/92 [==============================] - 8s 88ms/step - loss: 1.4909 - accuracy: 0.4687 - val_loss: 1.3238 - val_accuracy: 0.5360
Epoch 3/200
92/92 [==============================] - 8s 88ms/step - loss: 1.3041 - accuracy: 0.5474 - val_loss: 1.1958 - val_accuracy: 0.5920
Epoch 4/200
92/92 [==============================] - 8s 88ms/step - loss: 1.1770 - accuracy: 0.5968 - val_loss: 1.0476 - val_accuracy: 0.6567
Epoch 5/200
92/92 [==============================] - 8s 88ms/step - loss: 1.0772 - accuracy: 0.6311 - val_loss: 0.9850 - val_accuracy: 0.6773
Epoch 6/200
92/92 [==============================] - 8s 88ms/step - loss: 1.0108 - accuracy: 0.6578 - val_loss: 0.9322 - val_accuracy: 0.6920
Epoch 7/200
92/92 [==============================] - 8s 88ms/step - loss: 0.9583 - accuracy: 0.6776 - val_loss: 0.8516 - val_accuracy: 0.7267
Epoch 8/200
92/92 [==============================] - 8s 88ms/step - loss: 0.8963 - accuracy: 0.6998 - val_loss: 0.8104 - val_accuracy: 0.7393
Epoch 9/200
92/92 [==============================] - 8s 88ms/step - loss: 0.8570 - accuracy: 0.7169 - val_loss: 0.8398 - val_accuracy: 0.7340
Epoch 10/200
92/92 [==============================] - 8s 88ms/step - loss: 0.8273 - accuracy: 0.7264 - val_loss: 0.7797 - val_accuracy: 0.7500
Epoch 11/200
92/92 [==============================] - 8s 88ms/step - loss: 0.7802 - accuracy: 0.7416 - val_loss: 0.7468 - val_accuracy: 0.7690
Epoch 12/200
92/92 [==============================] - 8s 88ms/step - loss: 0.7483 - accuracy: 0.7539 - val_loss: 0.7221 - val_accuracy: 0.7720
Epoch 13/200
92/92 [==============================] - 8s 88ms/step - loss: 0.7185 - accuracy: 0.7670 - val_loss: 0.7534 - val_accuracy: 0.7563
Epoch 14/200
92/92 [==============================] - 8s 88ms/step - loss: 0.6979 - accuracy: 0.7731 - val_loss: 0.7291 - val_accuracy: 0.7720
Epoch 15/200
92/92 [==============================] - 8s 88ms/step - loss: 0.6816 - accuracy: 0.7788 - val_loss: 0.7192 - val_accuracy: 0.7733
Epoch 16/200
92/92 [==============================] - 8s 88ms/step - loss: 0.6573 - accuracy: 0.7889 - val_loss: 0.6892 - val_accuracy: 0.7803
Epoch 17/200
92/92 [==============================] - 8s 88ms/step - loss: 0.6305 - accuracy: 0.7979 - val_loss: 0.6888 - val_accuracy: 0.7907
Epoch 18/200
92/92 [==============================] - 8s 88ms/step - loss: 0.6132 - accuracy: 0.8036 - val_loss: 0.7068 - val_accuracy: 0.7790
Epoch 19/200
92/92 [==============================] - 8s 88ms/step - loss: 0.6047 - accuracy: 0.8076 - val_loss: 0.6800 - val_accuracy: 0.7887
Epoch 20/200
92/92 [==============================] - 8s 88ms/step - loss: 0.5821 - accuracy: 0.8160 - val_loss: 0.6849 - val_accuracy: 0.7803
Epoch 21/200
92/92 [==============================] - 8s 88ms/step - loss: 0.5570 - accuracy: 0.8251 - val_loss: 0.6717 - val_accuracy: 0.7897
Epoch 22/200
92/92 [==============================] - 8s 88ms/step - loss: 0.5409 - accuracy: 0.8321 - val_loss: 0.6633 - val_accuracy: 0.8007
Epoch 23/200
92/92 [==============================] - 8s 88ms/step - loss: 0.5350 - accuracy: 0.8335 - val_loss: 0.6592 - val_accuracy: 0.7960
Epoch 24/200
92/92 [==============================] - 8s 88ms/step - loss: 0.5083 - accuracy: 0.8423 - val_loss: 0.6586 - val_accuracy: 0.8010
Epoch 25/200
92/92 [==============================] - 8s 88ms/step - loss: 0.4993 - accuracy: 0.8474 - val_loss: 0.6549 - val_accuracy: 0.7997
Epoch 26/200
92/92 [==============================] - 8s 88ms/step - loss: 0.5041 - accuracy: 0.8465 - val_loss: 0.6829 - val_accuracy: 0.7930
Epoch 27/200
92/92 [==============================] - 8s 88ms/step - loss: 0.4815 - accuracy: 0.8538 - val_loss: 0.6603 - val_accuracy: 0.8020
Epoch 28/200
92/92 [==============================] - 8s 88ms/step - loss: 0.4683 - accuracy: 0.8579 - val_loss: 0.6643 - val_accuracy: 0.8017
Epoch 29/200
92/92 [==============================] - 8s 88ms/step - loss: 0.4591 - accuracy: 0.8633 - val_loss: 0.6608 - val_accuracy: 0.7927
Epoch 30/200
92/92 [==============================] - 8s 88ms/step - loss: 0.4462 - accuracy: 0.8676 - val_loss: 0.6713 - val_accuracy: 0.8043
Epoch 31/200
92/92 [==============================] - 8s 88ms/step - loss: 0.4416 - accuracy: 0.8695 - val_loss: 0.6637 - val_accuracy: 0.8070
Epoch 32/200
92/92 [==============================] - 8s 88ms/step - loss: 0.4266 - accuracy: 0.8753 - val_loss: 0.6774 - val_accuracy: 0.8040
Epoch 33/200
92/92 [==============================] - 8s 88ms/step - loss: 0.4191 - accuracy: 0.8774 - val_loss: 0.6679 - val_accuracy: 0.8017
Epoch 34/200
92/92 [==============================] - 8s 88ms/step - loss: 0.4155 - accuracy: 0.8814 - val_loss: 0.6854 - val_accuracy: 0.8003
Epoch 35/200
92/92 [==============================] - 8s 88ms/step - loss: 0.4000 - accuracy: 0.8866 - val_loss: 0.6808 - val_accuracy: 0.8020
Epoch 36/200
92/92 [==============================] - 8s 88ms/step - loss: 0.3986 - accuracy: 0.8872 - val_loss: 0.6827 - val_accuracy: 0.8047
Epoch 37/200
92/92 [==============================] - 8s 88ms/step - loss: 0.3860 - accuracy: 0.8920 - val_loss: 0.6774 - val_accuracy: 0.8027
Epoch 38/200
92/92 [==============================] - 8s 88ms/step - loss: 0.3793 - accuracy: 0.8954 - val_loss: 0.6856 - val_accuracy: 0.8050
Epoch 39/200
92/92 [==============================] - 8s 88ms/step - loss: 0.3710 - accuracy: 0.8961 - val_loss: 0.6902 - val_accuracy: 0.8067
Epoch 40/200
92/92 [==============================] - 8s 88ms/step - loss: 0.3707 - accuracy: 0.9002 - val_loss: 0.6739 - val_accuracy: 0.8087
Epoch 41/200
92/92 [==============================] - 8s 88ms/step - loss: 0.3657 - accuracy: 0.9014 - val_loss: 0.7075 - val_accuracy: 0.8090
Epoch 42/200
92/92 [==============================] - 8s 88ms/step - loss: 0.3659 - accuracy: 0.9013 - val_loss: 0.7132 - val_accuracy: 0.8080
Epoch 43/200
92/92 [==============================] - 8s 88ms/step - loss: 0.3440 - accuracy: 0.9102 - val_loss: 0.7143 - val_accuracy: 0.8073
Epoch 44/200
92/92 [==============================] - 8s 88ms/step - loss: 0.3433 - accuracy: 0.9092 - val_loss: 0.6964 - val_accuracy: 0.8097
Epoch 45/200
92/92 [==============================] - 8s 88ms/step - loss: 0.3361 - accuracy: 0.9133 - val_loss: 0.7172 - val_accuracy: 0.8003
Epoch 46/200
92/92 [==============================] - 8s 88ms/step - loss: 0.3419 - accuracy: 0.9118 - val_loss: 0.7165 - val_accuracy: 0.8107
Epoch 47/200
92/92 [==============================] - 8s 88ms/step - loss: 0.3347 - accuracy: 0.9145 - val_loss: 0.7256 - val_accuracy: 0.8070
Epoch 48/200
92/92 [==============================] - 8s 88ms/step - loss: 0.3311 - accuracy: 0.9158 - val_loss: 0.7293 - val_accuracy: 0.8110
Epoch 49/200
92/92 [==============================] - 8s 88ms/step - loss: 0.3348 - accuracy: 0.9158 - val_loss: 0.7216 - val_accuracy: 0.8090
Epoch 50/200
92/92 [==============================] - 8s 88ms/step - loss: 0.3243 - accuracy: 0.9187 - val_loss: 0.7128 - val_accuracy: 0.8117
Epoch 51/200
92/92 [==============================] - 8s 88ms/step - loss: 0.3192 - accuracy: 0.9229 - val_loss: 0.7343 - val_accuracy: 0.8057
Epoch 52/200
92/92 [==============================] - 8s 88ms/step - loss: 0.3234 - accuracy: 0.9205 - val_loss: 0.7181 - val_accuracy: 0.8143
Epoch 53/200
92/92 [==============================] - 8s 88ms/step - loss: 0.3147 - accuracy: 0.9242 - val_loss: 0.7392 - val_accuracy: 0.8090
Epoch 54/200
92/92 [==============================] - 8s 88ms/step - loss: 0.3101 - accuracy: 0.9257 - val_loss: 0.7448 - val_accuracy: 0.8113
Epoch 55/200
92/92 [==============================] - 8s 88ms/step - loss: 0.3068 - accuracy: 0.9264 - val_loss: 0.7618 - val_accuracy: 0.8000
Epoch 56/200
92/92 [==============================] - 8s 88ms/step - loss: 0.3054 - accuracy: 0.9281 - val_loss: 0.7329 - val_accuracy: 0.8070
Epoch 57/200
92/92 [==============================] - 8s 88ms/step - loss: 0.3074 - accuracy: 0.9276 - val_loss: 0.7236 - val_accuracy: 0.8170
Epoch 58/200
92/92 [==============================] - 8s 88ms/step - loss: 0.3080 - accuracy: 0.9265 - val_loss: 0.7443 - val_accuracy: 0.8100
Epoch 59/200
92/92 [==============================] - 8s 88ms/step - loss: 0.2957 - accuracy: 0.9316 - val_loss: 0.7611 - val_accuracy: 0.8067
Epoch 60/200
92/92 [==============================] - 8s 88ms/step - loss: 0.3038 - accuracy: 0.9294 - val_loss: 0.7704 - val_accuracy: 0.8043
Epoch 61/200
92/92 [==============================] - 8s 88ms/step - loss: 0.2909 - accuracy: 0.9337 - val_loss: 0.7742 - val_accuracy: 0.8107
Epoch 62/200
92/92 [==============================] - 8s 88ms/step - loss: 0.2954 - accuracy: 0.9334 - val_loss: 0.7745 - val_accuracy: 0.8033
Epoch 63/200
92/92 [==============================] - 8s 87ms/step - loss: 0.2977 - accuracy: 0.9317 - val_loss: 0.7697 - val_accuracy: 0.7990
Epoch 64/200
92/92 [==============================] - 8s 88ms/step - loss: 0.2869 - accuracy: 0.9363 - val_loss: 0.7729 - val_accuracy: 0.8080
Epoch 65/200
92/92 [==============================] - 8s 88ms/step - loss: 0.2955 - accuracy: 0.9336 - val_loss: 0.7796 - val_accuracy: 0.8060
Epoch 66/200
92/92 [==============================] - 8s 88ms/step - loss: 0.2877 - accuracy: 0.9371 - val_loss: 0.7631 - val_accuracy: 0.8027
Epoch 67/200
92/92 [==============================] - 8s 88ms/step - loss: 0.2796 - accuracy: 0.9399 - val_loss: 0.7857 - val_accuracy: 0.8063
Code Text

Model: "sequential_15"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_25 (Conv2D)           (None, 30, 30, 256)       7168      
_________________________________________________________________
max_pooling2d_22 (MaxPooling (None, 15, 15, 256)       0         
_________________________________________________________________
dropout_23 (Dropout)         (None, 15, 15, 256)       0         
_________________________________________________________________
conv2d_26 (Conv2D)           (None, 13, 13, 512)       1180160   
_________________________________________________________________
max_pooling2d_23 (MaxPooling (None, 6, 6, 512)         0         
_________________________________________________________________
dropout_24 (Dropout)         (None, 6, 6, 512)         0         
_________________________________________________________________
conv2d_27 (Conv2D)           (None, 4, 4, 1024)        4719616   
_________________________________________________________________
max_pooling2d_24 (MaxPooling (None, 2, 2, 1024)        0         
_________________________________________________________________
dropout_25 (Dropout)         (None, 2, 2, 1024)        0         
_________________________________________________________________
flatten_15 (Flatten)         (None, 4096)              0         
_________________________________________________________________
dense_38 (Dense)             (None, 384)               1573248   
_________________________________________________________________
dense_39 (Dense)             (None, 10)                3850      
=================================================================
Total params: 7,484,042
Trainable params: 7,484,042
Non-trainable params: 0
_________________________________________________________________
Code Text

313/313 [==============================] - 2s 5ms/step - loss: 0.8150 - accuracy: 0.7968
test set accuracy:  79.68000173568726
Code Text

shape of preds:  (10000, 10)

dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])





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       [ 55,   5, 682,  58,  63,  46,  38,  25,  24,   4],
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